Ingesting Information into a Universal Cognitive Graph

ABSTRACT

A computer-implementable method for ingesting information into a cognitive graph comprising: receiving data from a data source; determining whether the data comprises text; processing the data, the processing comprising performing a natural language processing operation on the data, the processing the data identifying a plurality of knowledge elements based upon the natural language processing operation; and, storing at least some of the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. Still more particularly, it relates to a method, system andcomputer-usable medium for performing cognitive inference and learningoperations.

Description of the Related Art

In general, “big data” refers to a collection of datasets so large andcomplex that they become difficult to process using typical databasemanagement tools and traditional data processing approaches. Thesedatasets can originate from a wide variety of sources, includingcomputer systems, mobile devices, credit card transactions, televisionbroadcasts, and medical equipment, as well as infrastructures associatedwith cities, sensor-equipped buildings and factories, and transportationsystems. Challenges commonly associated with big data, which may be acombination of structured, unstructured, and semi-structured data,include its capture, curation, storage, search, sharing, analysis andvisualization. In combination, these challenges make it difficult toefficiently process large quantities of data within tolerable timeintervals.

Nonetheless, big data analytics hold the promise of extracting insightsby uncovering difficult-to-discover patterns and connections, as well asproviding assistance in making complex decisions by analyzing differentand potentially conflicting options. As such, individuals andorganizations alike can be provided new opportunities to innovate,compete, and capture value.

One aspect of big data is “dark data,” which generally refers to datathat is either not collected, neglected, or underutilized. Examples ofdata that is not currently being collected includes location data priorto the emergence of companies such as Foursquare or social data prior tothe advent companies such as Facebook. An example of data that is beingcollected, but is difficult to access at the right time and place,includes data associated with the side effects of certain spider biteswhile on a camping trip. As another example, data that is collected andavailable, but has not yet been productized of fully utilized, mayinclude disease insights from population-wide healthcare records andsocial media feeds. As a result, a case can be made that dark data mayin fact be of higher value than big data in general, especially as itcan likely provide actionable insights when it is combined withreadily-available data.

SUMMARY OF THE INVENTION

A method, system and computer-usable medium are disclosed for cognitiveinference and learning operations.

In one embodiment, the invention relates to a computer-implementablemethod for ingesting information into a cognitive graph comprising:receiving data from a data source; determining whether the datacomprises text; processing the data, the processing comprisingperforming a natural language processing operation on the data, theprocessing the data identifying a plurality of knowledge elements basedupon the natural language processing operation; and, storing at leastsome of the knowledge elements within the cognitive graph as acollection of knowledge elements, the storing universally representingknowledge obtained from the data.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts an exemplary client computer in which the presentinvention may be implemented;

FIG. 2 is a simplified block diagram of a cognitive inference andlearning system (CILS);

FIG. 3 is a simplified block diagram of a CILS reference modelimplemented in accordance with an embodiment of the invention;

FIGS. 4a through 4c depict additional components of the CILS referencemodel shown in FIG. 3;

FIG. 5 is a simplified process diagram of CILS operations;

FIG. 6 depicts the lifecycle of CILS agents implemented to perform CILSoperations;

FIG. 7 is a simplified block diagram of a universal knowledge repositoryused to perform CILS operations;

FIGS. 8a through 8c are a simplified block diagram of the performance ofoperations related to the use of a universal knowledge repository by aCILS for the generation of cognitive insights;

FIG. 9 is a simplified depiction of a universal schema;

FIG. 10 depicts the use of diamond and ladder entailment patterns toaccurately and precisely model knowledge elements in a universalcognitive graph;

FIGS. 11a through 11d are a simplified graphical representation ofquantity modeled as knowledge elements in a universal cognitive graph;

FIGS. 12a through 12d are a simplified graphical representation oflocation, time and scale modeled as knowledge elements in a universalcognitive graph;

FIGS. 13a and 13b are a simplified graphical representation of verbsmodeled as knowledge elements in a universal cognitive graph;

FIGS. 14a and 14b are a simplified graphical representation of themodeling of negation of in a universal cognitive graph;

FIGS. 15a through 15e are a simplified graphical representation of acorpus of text modeled as knowledge elements in a universal cognitivegraph to represent an associated natural language concept;

FIG. 16 is a simplified block diagram of a plurality of cognitiveplatforms implemented in a hybrid cloud environment; and

FIGS. 17a and 17b are a simplified process flow diagram of thegeneration of composite cognitive insights by a CILS.

DETAILED DESCRIPTION

A method, system and computer-usable medium are disclosed for cognitiveinference and learning operations. The present invention may be asystem, a method, and/or a computer program product. The computerprogram product may include a computer readable storage medium (ormedia) having computer readable program instructions thereon for causinga processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 is a generalized illustration of an information processing system100 that can be used to implement the system and method of the presentinvention. The information processing system 100 includes a processor(e.g., central processor unit or “CPU”) 102, input/output (I/O) devices104, such as a display, a keyboard, a mouse, and associated controllers,a hard drive or disk storage 106, and various other subsystems 108. Invarious embodiments, the information processing system 100 also includesnetwork port 110 operable to connect to a network 140, which is likewiseaccessible by a service provider server 142. The information processingsystem 100 likewise includes system memory 112, which is interconnectedto the foregoing via one or more buses 114. System memory 112 furthercomprises operating system (OS) 116 and in various embodiments may alsocomprise cognitive inference and learning system (CILS) 117. In theseand other embodiments, the CILS 117 may likewise comprise inventionmodules 120. In one embodiment, the information processing system 100 isable to download the CILS 117 from the service provider server 142. Inanother embodiment, the CILS 117 is provided as a service from theservice provider server 142.

In various embodiments, the CILS 117 is implemented to perform variouscognitive computing operations described in greater detail herein. Asused herein, cognitive computing broadly refers to a class of computinginvolving self-learning systems that use techniques such as spatialnavigation, machine vision, and pattern recognition to increasinglymimic the way the human brain works. To be more specific, earlierapproaches to computing typically solved problems by executing a set ofinstructions codified within software. In contrast, cognitive computingapproaches are data-driven, sense-making, insight-extracting,problem-solving systems that have more in common with the structure ofthe human brain than with the architecture of contemporary,instruction-driven computers.

To further differentiate these distinctions, traditional computers mustfirst be programmed by humans to perform specific tasks, while cognitivesystems learn from their interactions with data and humans alike, and ina sense, program themselves to perform new tasks. To summarize thedifference between the two, traditional computers are designed tocalculate rapidly. Cognitive systems are designed to quickly drawinferences from data and gain new knowledge.

Cognitive systems achieve these abilities by combining various aspectsof artificial intelligence, natural language processing, dynamiclearning, and hypothesis generation to render vast quantities ofintelligible data to assist humans in making better decisions. As such,cognitive systems can be characterized as having the ability to interactnaturally with people to extend what either humans, or machines, coulddo on their own. Furthermore, they are typically able to process naturallanguage, multi-structured data, and experience much in the same way ashumans. Moreover, they are also typically able to learn a knowledgedomain based upon the best available data and get better, and moreimmersive, over time.

It will be appreciated that more data is currently being produced everyday than was recently produced by human beings from the beginning ofrecorded time. Deep within this ever-growing mass of data is a class ofdata known as “dark data,” which includes neglected information, ambientsignals, and insights that can assist organizations and individuals inaugmenting their intelligence and deliver actionable insights throughthe implementation of cognitive applications. As used herein, cognitiveapplications, or “cognitive apps,” broadly refer to cloud-based, bigdata interpretive applications that learn from user engagement and datainteractions. Such cognitive applications extract patterns and insightsfrom dark data sources that are currently almost completely opaque.Examples of such dark data include disease insights from population-widehealthcare records and social media feeds, or from new sources ofinformation, such as sensors monitoring pollution in delicate marineenvironments.

Over time, it is anticipated that cognitive applications willfundamentally change the ways in which many organizations operate asthey invert current issues associated with data volume and variety toenable a smart, interactive data supply chain. Ultimately, cognitiveapplications hold the promise of receiving a user query and immediatelyproviding a data-driven answer from a masked data supply chain inresponse. As they evolve, it is likewise anticipated that cognitiveapplications may enable a new class of “sixth sense” applications thatintelligently detect and learn from relevant data and events to offerinsights, predictions and advice rather than wait for commands. Just asweb and mobile applications changed the way people access data,cognitive applications may change the way people listen to, and becomeempowered by, multi-structured data such as emails, social media feeds,doctors notes, transaction records, and call logs.

However, the evolution of such cognitive applications has associatedchallenges, such as how to detect events, ideas, images, and othercontent that may be of interest. For example, assuming that the role andpreferences of a given user are known, how is the most relevantinformation discovered, prioritized, and summarized from large streamsof multi-structured data such as news feeds, blogs, social media,structured data, and various knowledge bases? To further the example,what can a healthcare executive be told about their competitor's marketshare? Other challenges include the creation of acontextually-appropriate visual summary of responses to questions orqueries.

FIG. 2 is a simplified block diagram of a cognitive inference andlearning system (CILS) implemented in accordance with an embodiment ofthe invention. In various embodiments, the CILS 117 is implemented toincorporate a variety of processes, including semantic analysis 202,goal optimization 204, collaborative filtering 206, common sensereasoning 208, natural language processing 210, summarization 212,temporal/spatial reasoning 214, and entity resolution 216 to generatecognitive insights.

As used herein, semantic analysis 202 broadly refers to performingvarious analysis operations to achieve a semantic level of understandingabout language by relating syntactic structures. In various embodiments,various syntactic structures are related from the levels of phrases,clauses, sentences and paragraphs, to the level of the body of contentas a whole and to its language-independent meaning. In certainembodiments, the semantic analysis 202 process includes processing atarget sentence to parse it into its individual parts of speech, tagsentence elements that are related to predetermined items of interest,identify dependencies between individual words, and perform co-referenceresolution. For example, if a sentence states that the author reallylikes the hamburgers served by a particular restaurant, then the name ofthe “particular restaurant” is co-referenced to “hamburgers.”

As likewise used herein, goal optimization 204 broadly refers toperforming multi-criteria decision making operations to achieve a givengoal or target objective. In various embodiments, one or more goaloptimization 204 processes are implemented by the CILS 117 to definepredetermined goals, which in turn contribute to the generation of acognitive insight. For example, goals for planning a vacation trip mayinclude low cost (e.g., transportation and accommodations), location(e.g., by the beach), and speed (e.g., short travel time). In thisexample, it will be appreciated that certain goals may be in conflictwith another. As a result, a cognitive insight provided by the CILS 117to a traveler may indicate that hotel accommodations by a beach may costmore than they care to spend.

Collaborative filtering 206, as used herein, broadly refers to theprocess of filtering for information or patterns through thecollaborative involvement of multiple agents, viewpoints, data sources,and so forth. The application of such collaborative filtering 206processes typically involves very large and different kinds of datasets, including sensing and monitoring data, financial data, and userdata of various kinds. Collaborative filtering 206 may also refer to theprocess of making automatic predictions associated with predeterminedinterests of a user by collecting preferences or other information frommany users. For example, if person ‘A’ has the same opinion as a person‘B’ for a given issue ‘x’, then an assertion can be made that person ‘A’is more likely to have the same opinion as person ‘B’ opinion on adifferent issue ‘y’ than to have the same opinion on issue ‘y’ as arandomly chosen person. In various embodiments, the collaborativefiltering 206 process is implemented with various recommendation enginesfamiliar to those of skill in the art to make recommendations.

As used herein, common sense reasoning 208 broadly refers to simulatingthe human ability to make deductions from common facts they inherentlyknow. Such deductions may be made from inherent knowledge about thephysical properties, purpose, intentions and possible behavior ofordinary things, such as people, animals, objects, devices, and so on.In various embodiments, common sense reasoning 208 processes areimplemented to assist the CILS 117 in understanding and disambiguatingwords within a predetermined context. In certain embodiments, the commonsense reasoning 208 processes are implemented to allow the CILS 117 togenerate text or phrases related to a target word or phrase to performdeeper searches for the same terms. It will be appreciated that if thecontext of a word is better understood, then a common senseunderstanding of the word can then be used to assist in finding betteror more accurate information. In certain embodiments, this better ormore accurate understanding of the context of a word, and its relatedinformation, allows the CILS 117 to make more accurate deductions, whichare in turn used to generate cognitive insights.

As likewise used herein, natural language processing (NLP) 210 broadlyrefers to interactions with a system, such as the CILS 117, through theuse of human, or natural, languages. In various embodiments, various NLP210 processes are implemented by the CILS 117 to achieve naturallanguage understanding, which enables it to not only derive meaning fromhuman or natural language input, but to also generate natural languageoutput.

Summarization 212, as used herein, broadly refers to processing a set ofinformation, organizing and ranking it, and then generating acorresponding summary. As an example, a news article may be processed toidentify its primary topic and associated observations, which are thenextracted, ranked, and then presented to the user. As another example,page ranking operations may be performed on the same news article toidentify individual sentences, rank them, order them, and determinewhich of the sentences are most impactful in describing the article andits content. As yet another example, a structured data record, such as apatient's electronic medical record (EMR), may be processed using thesummarization 212 process to generate sentences and phrases thatdescribes the content of the EMR. In various embodiments, varioussummarization 212 processes are implemented by the CILS 117 to generatesummarizations of content streams, which are in turn used to generatecognitive insights.

As used herein, temporal/spatial reasoning 214 broadly refers toreasoning based upon qualitative abstractions of temporal and spatialaspects of common sense knowledge, described in greater detail herein.For example, it is not uncommon for a predetermined set of data tochange over time. Likewise, other attributes, such as its associatedmetadata, may likewise change over time. As a result, these changes mayaffect the context of the data. To further the example, the context ofasking someone what they believe they should be doing at 3:00 in theafternoon during the workday while they are at work may be quitedifferent than asking the same user the same question at 3:00 on aSunday afternoon when they are at home. In certain embodiments, varioustemporal/spatial reasoning 214 processes are implemented by the CILS 117to determine the context of queries, and associated data, which are inturn used to generate cognitive insights.

As likewise used herein, entity resolution 216 broadly refers to theprocess of finding elements in a set of data that refer to the sameentity across different data sources (e.g., structured, non-structured,streams, devices, etc.), where the target entity does not share a commonidentifier. In various embodiments, the entity resolution 216 process isimplemented by the CILS 117 to identify significant nouns, adjectives,phrases or sentence elements that represent various predeterminedentities within one or more domains. From the foregoing, it will beappreciated that the implementation of one or more of the semanticanalysis 202, goal optimization 204, collaborative filtering 206, commonsense reasoning 208, natural language processing 210, summarization 212,temporal/spatial reasoning 214, and entity resolution 216 processes bythe CILS 117 can facilitate the generation of a semantic, cognitivemodel.

In various embodiments, the CILS 117 receives ambient signals 220,curated data 222, and learned knowledge 224, which is then processed bythe CILS 117 to generate one or more cognitive graphs 226. In turn, theone or more cognitive graphs 226 are further used by the CILS 117 togenerate cognitive insight streams, which are then delivered to one ormore destinations 230, as described in greater detail herein.

As used herein, ambient signals 220 broadly refer to input signals, orother data streams, that may contain data providing additional insightor context to the curated data 222 and learned knowledge 224 received bythe CILS 117. For example, ambient signals may allow the CILS 117 tounderstand that a user is currently using their mobile device, atlocation ‘x’, at time ‘y’, doing activity ‘z’. To further the example,there is a difference between the user using their mobile device whilethey are on an airplane versus using their mobile device after landingat an airport and walking between one terminal and another. To extendthe example even further, ambient signals may add additional context,such as the user is in the middle of a three leg trip and has two hoursbefore their next flight. Further, they may be in terminal A1, but theirnext flight is out of C1, it is lunchtime, and they want to know thebest place to eat. Given the available time the user has, their currentlocation, restaurants that are proximate to their predicted route, andother factors such as food preferences, the CILS 117 can perform variouscognitive operations and provide a recommendation for where the user caneat.

In various embodiments, the curated data 222 may include structured,unstructured, social, public, private, streaming, device or other typesof data described in greater detail herein. In certain embodiments, thelearned knowledge 224 is based upon past observations and feedback fromthe presentation of prior cognitive insight streams and recommendations.In various embodiments, the learned knowledge 224 is provided via afeedback look that provides the learned knowledge 224 in the form of alearning stream of data.

As likewise used herein, a cognitive graph 226 refers to arepresentation of expert knowledge, associated with individuals andgroups over a period of time, to depict relationships between people,places, and things using words, ideas, audio and images. As such, acognitive graph is a machine-readable formalism for knowledgerepresentation that provides a common framework allowing data andknowledge to be shared and reused across user, application,organization, and community boundaries. In certain embodiments, thecognitive graph includes integrated machine learning functionality. Incertain embodiments, the machine learning functionality includescognitive functionality which uses feedback to improve the accuracy ofknowledge stored within the cognitive graph. In certain embodiments, thecognitive graph is configured to seamlessly function with a cognitivesystem such as the cognitive inference and learning system 118.

In various embodiments, the information contained in, and referenced by,a cognitive graph 226 is derived from many sources (e.g., public,private, social, device), such as curated data 222. In certain of theseembodiments, the cognitive graph 226 assists in the identification andorganization of information associated with how people, places andthings are related to one other. In various embodiments, the cognitivegraph 226 enables automated agents, described in greater detail herein,to access the Web more intelligently, enumerate inferences throughutilization of curated, structured data 222, and provide answers toquestions by serving as a computational knowledge engine.

In certain embodiments, the cognitive graph 226 not only elicits andmaps expert knowledge by deriving associations from data, it alsorenders higher level insights and accounts for knowledge creationthrough collaborative knowledge modeling. In various embodiments, thecognitive graph 226 is a machine-readable, declarative memory systemthat stores and learns both episodic memory (e.g., specific personalexperiences associated with an individual or entity), and semanticmemory, which stores factual information (e.g., geo location of anairport or restaurant).

For example, the cognitive graph 226 may know that a given airport is aplace, and that there is a list of related places such as hotels,restaurants and departure gates. Furthermore, the cognitive graph 226may know that people such as business travelers, families and collegestudents use the airport to board flights from various carriers, eat atvarious restaurants, or shop at certain retail stores. The cognitivegraph 226 may also have knowledge about the key attributes from variousretail rating sites that travelers have used to describe the food andtheir experience at various venues in the airport over the past sixmonths.

In certain embodiments, the cognitive insight stream 228 isbidirectional, and supports flows of information both too and fromdestinations 230. In these embodiments, the first flow is generated inresponse to receiving a query, and subsequently delivered to one or moredestinations 230. The second flow is generated in response to detectinginformation about a user of one or more of the destinations 230. Suchuse results in the provision of information to the CILS 117. Inresponse, the CILS 117 processes that information, in the context ofwhat it knows about the user, and provides additional information to theuser, such as a recommendation. In various embodiments, the cognitiveinsight stream 228 is configured to be provided in a “push” streamconfiguration familiar to those of skill in the art. In certainembodiments, the cognitive insight stream 228 is implemented to usenatural language approaches familiar to skilled practitioners of the artto support interactions with a user.

In various embodiments, the cognitive insight stream 228 may include astream of visualized insights. As used herein, visualized insightsbroadly refers to cognitive insights that are presented in a visualmanner, such as a map, an infographic, images, and so forth. In certainembodiments, these visualized insights may include various cognitiveinsights, such as “What happened?”, “What do I know about it?”, “What islikely to happen next?”, or “What should I do about it?” In theseembodiments, the cognitive insight stream is generated by variouscognitive agents, which are applied to various sources, datasets, andcognitive graphs 226. As used herein, a cognitive agent broadly refersto a computer program that performs a task with minimum specificdirections from users and learns from each interaction with data andhuman users.

In various embodiments, the CILS 117 delivers Cognition as a Service(CaaS). As such, it provides a cloud-based development and executionplatform that allow various cognitive applications and services tofunction more intelligently and intuitively. In certain embodiments,cognitive applications powered by the CILS 117 are able to think andinteract with users as intelligent virtual assistants. As a result,users are able to interact with such cognitive applications by askingthem questions and giving them commands. In response, these cognitiveapplications will be able to assist the user in completing tasks andmanaging their work more efficiently.

In these and other embodiments, the CILS 117 can operate as an analyticsplatform to process big data, and dark data as well, to provide dataanalytics through a public, private or hybrid cloud environment. As usedherein, cloud analytics broadly refers to a service model wherein datasources, data models, processing applications, computing power, analyticmodels, and sharing or storage of results are implemented within a cloudenvironment to perform one or more aspects of analytics.

In various embodiments, users submit queries and computation requests ina natural language format to the CILS 117. In response, they areprovided with a ranked list of relevant answers and aggregatedinformation with useful links and pertinent visualizations through agraphical representation. In these embodiments, the cognitive graph 226generates semantic and temporal maps to reflect the organization ofunstructured data and to facilitate meaningful learning from potentiallymillions of lines of text, much in the same way as arbitrary syllablesstrung together create meaning through the concept of language.

FIG. 3 is a simplified block diagram of a cognitive inference andlearning system (CILS) reference model implemented in accordance with anembodiment of the invention. In this embodiment, the CILS referencemodel is associated with the CILS 117 shown in FIG. 2. As shown in FIG.3, the CILS 117 includes client applications 302, applicationaccelerators 306, a cognitive platform 310, and cloud infrastructure340. In various embodiments, the client applications 302 includecognitive applications 304, which are implemented to understand andadapt to the user, not the other way around, by natively accepting andunderstanding human forms of communication, such as natural languagetext, audio, images, video, and so forth.

In these and other embodiments, the cognitive applications 304 possesssituational and temporal awareness based upon ambient signals from usersand data, which facilitates understanding the user's intent, content,context and meaning to drive goal-driven dialogs and outcomes. Further,they are designed to gain knowledge over time from a wide variety ofstructured, non-structured, and device data sources, continuouslyinterpreting and autonomously reprogramming themselves to betterunderstand a given domain. As such, they are well-suited to supporthuman decision making, by proactively providing trusted advice, offersand recommendations while respecting user privacy and permissions.

In various embodiments, the application accelerators 306 include acognitive application framework 308. In certain embodiments, theapplication accelerators 306 and the cognitive application framework 308support various plug-ins and components that facilitate the creation ofclient applications 302 and cognitive applications 304. In variousembodiments, the application accelerators 306 include widgets, userinterface (UI) components, reports, charts, and back-end integrationcomponents familiar to those of skill in the art.

As likewise shown in FIG. 3, the cognitive platform 310 includes amanagement console 312, a development environment 314, applicationprogram interfaces (APIs) 316, sourcing agents 317, a cognitive engine320, destination agents 336, and platform data 338, all of which aredescribed in greater detail herein. In various embodiments, themanagement console 312 is implemented to manage accounts and projects,along with user-specific metadata that is used to drive processes andoperations within the cognitive platform 310 for a predeterminedproject.

In certain embodiments, the development environment 314 is implementedto create custom extensions to the CILS 117 shown in FIG. 2. In variousembodiments, the development environment 314 is implemented for thedevelopment of a custom application, which may subsequently be deployedin a public, private or hybrid cloud environment. In certainembodiments, the development environment 314 is implemented for thedevelopment of a custom sourcing agent, a custom bridging agent, acustom destination agent, or various analytics applications orextensions.

In various embodiments, the APIs 316 are implemented to build and managepredetermined cognitive applications 304, described in greater detailherein, which are then executed on the cognitive platform 310 togenerate cognitive insights. Likewise, the sourcing agents 317 areimplemented in various embodiments to source a variety of multi-site,multi-structured source streams of data described in greater detailherein. In various embodiments, the cognitive engine 320 includes adataset engine 322, a graph query engine 326, an insight/learning engine330, and foundation components 334. In certain embodiments, the datasetengine 322 is implemented to establish and maintain a dynamic dataingestion and enrichment pipeline. In these and other embodiments, thedataset engine 322 may be implemented to orchestrate one or moresourcing agents 317 to source data. Once the data is sourced, the dataset engine 322 performs data enriching and other data processingoperations, described in greater detail herein, and generates one ormore sub-graphs that are subsequently incorporated into a targetcognitive graph.

In various embodiments, the graph query engine 326 is implemented toreceive and process queries such that they can be bridged into acognitive graph, as described in greater detail herein, through the useof a bridging agent. In certain embodiments, the graph query engine 326performs various natural language processing (NLP), familiar to skilledpractitioners of the art, to process the queries. In variousembodiments, the insight/learning engine 330 is implemented toencapsulate a predetermined algorithm, which is then applied to acognitive graph to generate a result, such as a cognitive insight or arecommendation. In certain embodiments, one or more such algorithms maycontribute to answering a specific question and provide additionalcognitive insights or recommendations. In various embodiments, two ormore of the dataset engine 322, the graph query engine 326, and theinsight/learning engine 330 may be implemented to operatecollaboratively to generate a cognitive insight or recommendation. Incertain embodiments, one or more of the dataset engine 322, the graphquery engine 326, and the insight/learning engine 330 may operateautonomously to generate a cognitive insight or recommendation.

The foundation components 334 shown in FIG. 3 include various reusablecomponents, familiar to those of skill in the art, which are used invarious embodiments to enable the dataset engine 322, the graph queryengine 326, and the insight/learning engine 330 to perform theirrespective operations and processes. Examples of such foundationcomponents 334 include natural language processing (NLP) components andcore algorithms, such as cognitive algorithms.

In various embodiments, the platform data 338 includes various datarepositories, described in greater detail herein, that are accessed bythe cognitive platform 310 to generate cognitive insights. In variousembodiments, the destination agents 336 are implemented to publishcognitive insights to a consumer of cognitive insight data. Examples ofsuch consumers of cognitive insight data include target databases,business intelligence applications, and mobile applications. It will beappreciated that many such examples of cognitive insight data consumersare possible and the foregoing is not intended to limit the spirit,scope or intent of the invention. In various embodiments, as describedin greater detail herein, the cloud infrastructure 340 includescognitive cloud management 342 components and cloud analyticsinfrastructure components 344.

FIGS. 4a through 4c depict additional cognitive inference and learningsystem (CILS) components implemented in accordance with an embodiment ofthe CILS reference model shown in FIG. 3. In this embodiment, the CILSreference model includes client applications 302, applicationaccelerators 306, a cognitive platform 310, and cloud infrastructure340. As shown in FIG. 4a , the client applications 302 include cognitiveapplications 304. In various embodiments, the cognitive applications 304are implemented natively accept and understand human forms ofcommunication, such as natural language text, audio, images, video, andso forth. In certain embodiments, the cognitive applications 304 mayinclude healthcare 402, business performance 403, travel 404, andvarious other 405 applications familiar to skilled practitioners of theart. As such, the foregoing is only provided as examples of suchcognitive applications 304 and is not intended to limit the intent,spirit of scope of the invention.

In various embodiments, the application accelerators 306 include acognitive application framework 308. In certain embodiments, theapplication accelerators 308 and the cognitive application framework 308support various plug-ins and components that facilitate the creation ofclient applications 302 and cognitive applications 304. In variousembodiments, the application accelerators 306 include widgets, userinterface (UI) components, reports, charts, and back-end integrationcomponents familiar to those of skill in the art. It will be appreciatedthat many such application accelerators 306 are possible and theirprovided functionality, selection, provision and support are a matter ofdesign choice. As such, the application accelerators 306 described ingreater detail herein are not intended to limit the spirit, scope orintent of the invention.

As shown in FIGS. 4a and 4b , the cognitive platform 310 includes amanagement console 312, a development environment 314, applicationprogram interfaces (APIs) 316, sourcing agents 317, a cognitive engine320, destination agents 336, platform data 338, and a crawl framework452. In various embodiments, the management console 312 is implementedto manage accounts and projects, along with management metadata 461 thatis used to drive processes and operations within the cognitive platform310 for a predetermined project.

In various embodiments, the management console 312 is implemented to runvarious services on the cognitive platform 310. In certain embodiments,the management console 312 is implemented to manage the configuration ofthe cognitive platform 310. In certain embodiments, the managementconsole 312 is implemented to establish the development environment 314.In various embodiments, the management console 312 may be implemented tomanage the development environment 314 once it is established. Skilledpractitioners of the art will realize that many such embodiments arepossible and the foregoing is not intended to limit the spirit, scope orintent of the invention.

In various embodiments, the development environment 314 is implementedto create custom extensions to the CILS 117 shown in FIG. 2. In theseand other embodiments, the development environment 314 is implemented tosupport various programming languages, such as Python, Java, R, andothers familiar to skilled practitioners of the art. In variousembodiments, the development environment 314 is implemented to allow oneor more of these various programming languages to create a variety ofanalytic models and applications. As an example, the developmentenvironment 314 may be implemented to support the R programminglanguage, which in turn can be used to create an analytic model that isthen hosted on the cognitive platform 310.

In certain embodiments, the development environment 314 is implementedfor the development of various custom applications or extensions relatedto the cognitive platform 310, which may subsequently be deployed in apublic, private or hybrid cloud environment. In various embodiments, thedevelopment environment 314 is implemented for the development ofvarious custom sourcing agents 317, custom enrichment agents 425, custombridging agents 429, custom insight agents 433, custom destinationagents 336, and custom learning agents 434, which are described ingreater detail herein.

In various embodiments, the APIs 316 are implemented to build and managepredetermined cognitive applications 304, described in greater detailherein, which are then executed on the cognitive platform 310 togenerate cognitive insights. In these embodiments, the APIs 316 mayinclude one or more of a project and dataset API 408, a cognitive searchAPI 409, a cognitive insight API 410, and other APIs. The selection ofthe individual APIs 316 implemented in various embodiments is a matterdesign choice and the foregoing is not intended to limit the spirit,scope or intent of the invention.

In various embodiments, the project and dataset API 408 is implementedwith the management console 312 to enable the management of a variety ofdata and metadata associated with various cognitive insight projects anduser accounts hosted or supported by the cognitive platform 310. In oneembodiment, the data and metadata managed by the project and dataset API408 are associated with billing information familiar to those of skillin the art. In one embodiment, the project and dataset API 408 is usedto access a data stream that is created, configured and orchestrated, asdescribed in greater detail herein, by the dataset engine 322.

In various embodiments, the cognitive search API 409 uses naturallanguage processes familiar to those of skill in the art to search atarget cognitive graph. Likewise, the cognitive insight API 410 isimplemented in various embodiments to configure the insight/learningengine 330 to provide access to predetermined outputs from one or morecognitive graph algorithms that are executing in the cognitive platform310. In certain embodiments, the cognitive insight API 410 isimplemented to subscribe to, or request, such predetermined outputs.

In various embodiments, the sourcing agents 317 may include a batchupload 414 agent, an API connectors 415 agent, a real-time streams 416agent, a Structured Query Language (SQL)/Not Only SQL (NoSQL) databases417 agent, a message engines 417 agent, and one or more custom sourcing420 agents. Skilled practitioners of the art will realize that othertypes of sourcing agents 317 may be used in various embodiments and theforegoing is not intended to limit the spirit, scope or intent of theinvention. In various embodiments, the sourcing agents 317 areimplemented to source a variety of multi-site, multi-structured sourcestreams of data described in greater detail herein. In certainembodiments, each of the sourcing agents 317 has a corresponding API.

In various embodiments, the batch uploading 414 agent is implemented forbatch uploading of data to the cognitive platform 310. In theseembodiments, the uploaded data may include a single data element, asingle data record or file, or a plurality of data records or files. Incertain embodiments, the data may be uploaded from more than one sourceand the uploaded data may be in a homogenous or heterogeneous form. Invarious embodiments, the API connectors 415 agent is implemented tomanage interactions with one or more predetermined APIs that areexternal to the cognitive platform 310. As an example, Associated Press®may have their own API for news stories, Expedia® for travelinformation, or the National Weather Service for weather information. Inthese examples, the API connectors 415 agent would be implemented todetermine how to respectively interact with each organization's API suchthat the cognitive platform 310 can receive information.

In various embodiments, the real-time streams 416 agent is implementedto receive various streams of data, such as social media streams (e.g.,Twitter feeds) or other data streams (e.g., device data streams). Inthese embodiments, the streams of data are received in near-real-time.In certain embodiments, the data streams include temporal attributes. Asan example, as data is added to a blog file, it is time-stamped tocreate temporal data. Other examples of a temporal data stream includeTwitter feeds, stock ticker streams, device location streams from adevice that is tracking location, medical devices tracking a patient'svital signs, and intelligent thermostats used to improve energyefficiency for homes.

In certain embodiments, the temporal attributes define a time window,which can be correlated to various elements of data contained in thestream. For example, as a given time window changes, associated data mayhave a corresponding change. In various embodiments, the temporalattributes do not define a time window. As an example, a social mediafeed may not have predetermined time windows, yet it is still temporal.As a result, the social media feed can be processed to determine whathappened in the last 24 hours, what happened in the last hour, whathappened in the last 15 minutes, and then determine related subjectmatter that is trending.

In various embodiments, the SQL/NoSQL databases 417 agent is implementedto interact with one or more target databases familiar to those of skillin the art. For example, the target database may include a SQL, NoSQL,delimited flat file, or other form of database. In various embodiments,the message engines 417 agent is implemented to provide data to thecognitive platform 310 from one or more message engines, such as amessage queue (MQ) system, a message bus, a message broker, anenterprise service bus (ESB), and so forth. Skilled practitioners of theart will realize that there are many such examples of message engineswith which the message engines 417 agent may interact and the foregoingis not intended to limit the spirit, scope or intent of the invention.

In various embodiments, the custom sourcing agents 420, which arepurpose-built, are developed through the use of the developmentenvironment 314, described in greater detail herein. Examples of customsourcing agents 420 include sourcing agents for various electronicmedical record (EMR) systems at various healthcare facilities. Such EMRsystems typically collect a variety of healthcare information, much ofit the same, yet it may be collected, stored and provided in differentways. In this example, the custom sourcing agents 420 allow thecognitive platform 310 to receive information from each disparatehealthcare source.

In various embodiments, the cognitive engine 320 includes a datasetengine 322, a graph engine 326, an insight/learning engine 330, learningagents 434, and foundation components 334. In these and otherembodiments, the dataset engine 322 is implemented as described ingreater detail to establish and maintain a dynamic data ingestion andenrichment pipeline. In various embodiments, the dataset engine 322 mayinclude a pipelines 422 component, an enrichment 423 component, astorage component 424, and one or more enrichment agents 425.

In various embodiments, the pipelines 422 component is implemented toingest various data provided by the sourcing agents 317. Once ingested,this data is converted by the pipelines 422 component into streams ofdata for processing. In certain embodiments, these managed streams areprovided to the enrichment 423 component, which performs data enrichmentoperations familiar to those of skill in the art. As an example, a datastream may be sourced from Associated Press® by a sourcing agent 317 andprovided to the dataset engine 322. The pipelines 422 component receivesthe data stream and routes it to the enrichment 423 component, whichthen enriches the data stream by performing sentiment analysis,geotagging, and entity detection operations to generate an enriched datastream. In certain embodiments, the enrichment operations includefiltering operations familiar to skilled practitioners of the art. Tofurther the preceding example, the Associated Press® data stream may befiltered by a predetermined geography attribute to generate an enricheddata stream.

The enriched data stream is then subsequently stored, as described ingreater detail herein, in a predetermined location. In variousembodiments, the enriched data stream is cached by the storage 424component to provide a local version of the enriched data stream. Incertain embodiments, the cached, enriched data stream is implemented tobe “replayed” by the cognitive engine 320. In one embodiment, thereplaying of the cached, enriched data stream allows incrementalingestion of the enriched data stream instead of ingesting the entireenriched data stream at one time. In various embodiments, one or moreenrichment agents 425 are implemented to be invoked by the enrichmentcomponent 423 to perform one or more enrichment operations described ingreater detail herein.

In various embodiments, the graph query engine 326 is implemented toreceive and process queries such that they can be bridged into acognitive graph, as described in greater detail herein, through the useof a bridging agent. In these embodiments, the graph query engine mayinclude a query 426 component, a translate 427 component, a bridge 428component, and one or more bridging agents 429.

In various embodiments, the query 426 component is implemented tosupport natural language queries. In these and other embodiments, thequery 426 component receives queries, processes them (e.g., using NLPprocesses), and then maps the processed query to a target cognitivegraph. In various embodiments, the translate 427 component isimplemented to convert the processed queries provided by the query 426component into a form that can be used to query a target cognitivegraph. To further differentiate the distinction between thefunctionality respectively provided by the query 426 and translate 427components, the query 426 component is oriented toward understanding aquery from a user. In contrast, the translate 427 component is orientedto translating a query that is understood into a form that can be usedto query a cognitive graph.

In various embodiments, the bridge 428 component is implemented togenerate an answer to a query provided by the translate 427 component.In certain embodiments, the bridge 428 component is implemented toprovide domain-specific responses when bridging a translated query to acognitive graph. For example, the same query bridged to a targetcognitive graph by the bridge 428 component may result in differentanswers for different domains, dependent upon domain-specific bridgingoperations performed by the bridge 428 component.

To further differentiate the distinction between the translate 427component and the bridging 428 component, the translate 427 componentrelates to a general domain translation of a question. In contrast, thebridging 428 component allows the question to be asked in the context ofa specific domain (e.g., healthcare, travel, etc.), given what is knownabout the data. In certain embodiments, the bridging 428 component isimplemented to process what is known about the translated query, in thecontext of the user, to provide an answer that is relevant to a specificdomain.

As an example, a user may ask, “Where should I eat today?” If the userhas been prescribed a particular health regimen, the bridging 428component may suggest a restaurant with a “heart healthy” menu. However,if the user is a business traveler, the bridging 428 component maysuggest the nearest restaurant that has the user's favorite food. Invarious embodiments, the bridging 428 component may provide answers, orsuggestions, that are composed and ranked according to a specific domainof use. In various embodiments, the bridging agent 429 is implemented tointeract with the bridging component 428 to perform bridging operationsdescribed in greater detail herein. In these embodiments, the bridgingagent interprets a translated query generated by the query 426 componentwithin a predetermined user context, and then maps it to predeterminednodes and links within a target cognitive graph.

In various embodiments, the insight/learning engine 330 is implementedto encapsulate a predetermined algorithm, which is then applied to atarget cognitive graph to generate a result, such as a cognitive insightor a recommendation. In certain embodiments, one or more such algorithmsmay contribute to answering a specific question and provide additionalcognitive insights or recommendations. In these and other embodiments,the insight/learning engine 330 is implemented to performinsight/learning operations, described in greater detail herein. Invarious embodiments, the insight/learning engine 330 may include adiscover/visibility 430 component, a predict 431 component, arank/recommend 432 component, and one or more insight 433 agents.

In various embodiments, the discover/visibility 430 component isimplemented to provide detailed information related to a predeterminedtopic, such as a subject or an event, along with associated historicalinformation. In certain embodiments, the predict 431 component isimplemented to perform predictive operations to provide insight intowhat may next occur for a predetermined topic. In various embodiments,the rank/recommend 432 component is implemented to perform ranking andrecommendation operations to provide a user prioritized recommendationsassociated with a provided cognitive insight.

In certain embodiments, the insight/learning engine 330 may includeadditional components. For example the additional components may includeclassification algorithms, clustering algorithms, and so forth. Skilledpractitioners of the art will realize that many such additionalcomponents are possible and that the foregoing is not intended to limitthe spirit, scope or intent of the invention. In various embodiments,the insights agents 433 are implemented to create a visual data story,highlighting user-specific insights, relationships and recommendations.As a result, it can share, operationalize, or track business insights invarious embodiments. In various embodiments, the learning agent 434 workin the background to continually update the cognitive graph, asdescribed in greater detail herein, from each unique interaction withdata and users.

In various embodiments, the destination agents 336 are implemented topublish cognitive insights to a consumer of cognitive insight data.Examples of such consumers of cognitive insight data include targetdatabases, business intelligence applications, and mobile applications.In various embodiments, the destination agents 336 may include aHypertext Transfer Protocol (HTTP) stream 440 agent, an API connectors441 agent, a databases 442 agent, a message engines 443 agent, a mobilepush notification 444 agent, and one or more custom destination 446agents. Skilled practitioners of the art will realize that other typesof destination agents 317 may be used in various embodiments and theforegoing is not intended to limit the spirit, scope or intent of theinvention. In certain embodiments, each of the destination agents 317has a corresponding API.

In various embodiments, the HTTP stream 440 agent is implemented forproviding various HTTP streams of cognitive insight data to apredetermined cognitive data consumer. In these embodiments, theprovided HTTP streams may include various HTTP data elements familiar tothose of skill in the art. In certain embodiments, the HTTP streams ofdata are provided in near-real-time. In various embodiments, the APIconnectors 441 agent is implemented to manage interactions with one ormore predetermined APIs that are external to the cognitive platform 310.As an example, various target databases, business intelligenceapplications, and mobile applications may each have their own uniqueAPI.

In various embodiments, the databases 442 agent is implemented forprovision of cognitive insight data to one or more target databasesfamiliar to those of skill in the art. For example, the target databasemay include a SQL, NoSQL, delimited flat file, or other form ofdatabase. In these embodiments, the provided cognitive insight data mayinclude a single data element, a single data record or file, or aplurality of data records or files. In certain embodiments, the data maybe provided to more than one cognitive data consumer and the provideddata may be in a homogenous or heterogeneous form. In variousembodiments, the message engines 443 agent is implemented to providecognitive insight data to one or more message engines, such as a messagequeue (MQ) system, a message bus, a message broker, an enterpriseservice bus (ESB), and so forth. Skilled practitioners of the art willrealize that there are many such examples of message engines with whichthe message engines 443 agent may interact and the foregoing is notintended to limit the spirit, scope or intent of the invention.

In various embodiments, the custom destination agents 420, which arepurpose-built, are developed through the use of the developmentenvironment 314, described in greater detail herein. Examples of customdestination agents 420 include destination agents for various electronicmedical record (EMR) systems at various healthcare facilities. Such EMRsystems typically collect a variety of healthcare information, much ofit the same, yet it may be collected, stored and provided in differentways. In this example, the custom destination agents 420 allow such EMRsystems to receive cognitive insight data in a form they can use.

In various embodiments, data that has been cleansed, normalized andenriched by the dataset engine, as described in greater detail herein,is provided by a destination agent 336 to a predetermined destination,likewise described in greater detail herein. In these embodiments,neither the graph query engine 326 nor the insight/learning engine 330are implemented to perform their respective functions.

In various embodiments, the foundation components 334 are implemented toenable the dataset engine 322, the graph query engine 326, and theinsight/learning engine 330 to perform their respective operations andprocesses. In these and other embodiments, the foundation components 334may include an NLP core 436 component, an NLP services 437 component,and a dynamic pipeline engine 438. In various embodiments, the NLP core436 component is implemented to provide a set of predetermined NLPcomponents for performing various NLP operations described in greaterdetail herein.

In these embodiments, certain of these NLP core components are surfacedthrough the NLP services 437 component, while some are used aslibraries. Examples of operations that are performed with suchcomponents include dependency parsing, parts-of-speech tagging, sentencepattern detection, and so forth. In various embodiments, the NLPservices 437 component is implemented to provide various internal NLPservices, which are used to perform entity detection, summarization, andother operations, likewise described in greater detail herein. In theseembodiments, the NLP services 437 component is implemented to interactwith the NLP core 436 component to provide predetermined NLP services,such as summarizing a target paragraph.

In various embodiments, the dynamic pipeline engine 438 is implementedto interact with the dataset engine 322 to perform various operationsrelated to receiving one or more sets of data from one or more sourcingagents, apply enrichment to the data, and then provide the enriched datato a predetermined destination. In these and other embodiments, thedynamic pipeline engine 438 manages the distribution of these variousoperations to a predetermined compute cluster and tracks versioning ofthe data as it is processed across various distributed computingresources. In certain embodiments, the dynamic pipeline engine 438 isimplemented to perform data sovereignty management operations tomaintain sovereignty of the data.

In various embodiments, the platform data 338 includes various datarepositories, described in greater detail herein, that are accessed bythe cognitive platform 310 to generate cognitive insights. In theseembodiments, the platform data 338 repositories may include repositoriesof dataset metadata 456, cognitive graphs 457, models 459, crawl data460, and management metadata 461. In various embodiments, the datasetmetadata 456 is associated with curated data 458 contained in therepository of cognitive graphs 457. In these and other embodiments, therepository of dataset metadata 456 contains dataset metadata thatsupports operations performed by the storage 424 component of thedataset engine 322. For example, if a Mongo® NoSQL database with tenmillion items is being processed, and the cognitive platform 310 failsafter ingesting nine million of the items, then the dataset metadata 456may be able to provide a checkpoint that allows ingestion to continue atthe point of failure instead restarting the ingestion process.

Those of skill in the art will realize that the use of such datasetmetadata 456 in various embodiments allows the dataset engine 322 to bestateful. In certain embodiments, the dataset metadata 456 allowssupport of versioning. For example versioning may be used to trackversions of modifications made to data, such as in data enrichmentprocesses described in greater detail herein. As another example,geotagging information may have been applied to a set of data during afirst enrichment process, which creates a first version of enricheddata. Adding sentiment data to the same million records during a secondenrichment process creates a second version of enriched data. In thisexample, the dataset metadata stored in the dataset metadata 456provides tracking of the different versions of the enriched data and thedifferences between the two.

In various embodiments, the repository of cognitive graphs 457 isimplemented to store cognitive graphs generated, accessed, and updatedby the cognitive engine 320 in the process of generating cognitiveinsights. In various embodiments, the repository of cognitive graphs 457may include one or more repositories of curated data 458, described ingreater detail herein. In certain embodiments, the repositories ofcurated data 458 includes data that has been curated by one or moreusers, machine operations, or a combination of the two, by performingvarious sourcing, filtering, and enriching operations described ingreater detail herein. In these and other embodiments, the curated data458 is ingested by the cognitive platform 310 and then processed, aslikewise described in greater detail herein, to generate cognitiveinsights. In various embodiments, the repository of models 459 isimplemented to store models that are generated, accessed, and updated bythe cognitive engine 320 in the process of generating cognitiveinsights. As used herein, models broadly refer to machine learningmodels. In certain embodiments, the models include one or morestatistical models.

In various embodiments, the crawl framework 452 is implemented tosupport various crawlers 454 familiar to skilled practitioners of theart. In certain embodiments, the crawlers 454 are custom configured forvarious target domains. For example, different crawlers 454 may be usedfor various travel forums, travel blogs, travel news and other travelsites. In various embodiments, data collected by the crawlers 454 isprovided by the crawl framework 452 to the repository of crawl data 460.In these embodiments, the collected crawl data is processed and thenstored in a normalized form in the repository of crawl data 460. Thenormalized data is then provided to SQL/NoSQL database 417 agent, whichin turn provides it to the dataset engine 322. In one embodiment, thecrawl database 460 is a NoSQL database, such as Mongo®.

In various embodiments, the repository of management metadata 461 isimplemented to store user-specific metadata used by the managementconsole 312 to manage accounts (e.g., billing information) and projects.In certain embodiments, the user-specific metadata stored in therepository of management metadata 461 is used by the management console312 to drive processes and operations within the cognitive platform 310for a predetermined project. In various embodiments, the user-specificmetadata stored in the repository of management metadata 461 is used toenforce data sovereignty. It will be appreciated that many suchembodiments are possible and the foregoing is not intended to limit thespirit, scope or intent of the invention.

Referring now to FIG. 4c , the cloud infrastructure 340 may include acognitive cloud management 342 component and a cloud analyticsinfrastructure 344 component in various embodiments. Current examples ofa cloud infrastructure 340 include Amazon Web Services (AWS®), availablefrom Amazon.com® of Seattle, Wash., IBM® Softlayer, available fromInternational Business Machines of Armonk, N.Y., and Nebula/Openstack, ajoint project between Raskspace Hosting®, of Windcrest, Tex., and theNational Aeronautics and Space Administration (NASA). In theseembodiments, the cognitive cloud management 342 component may include amanagement playbooks 468 sub-component, a cognitive cloud managementconsole 469 sub-component, a data console 470 sub-component, an assetrepository 471 sub-component. In certain embodiments, the cognitivecloud management 342 component may include various other sub-components.

In various embodiments, the management playbooks 468 sub-component isimplemented to automate the creation and management of the cloudanalytics infrastructure 344 component along with various otheroperations and processes related to the cloud infrastructure 340. Asused herein, “management playbooks” broadly refers to any set ofinstructions or data, such as scripts and configuration data, that isimplemented by the management playbooks 468 sub-component to perform itsassociated operations and processes.

In various embodiments, the cognitive cloud management console 469sub-component is implemented to provide a user visibility and managementcontrols related to the cloud analytics infrastructure 344 componentalong with various other operations and processes related to the cloudinfrastructure 340. In various embodiments, the data console 470sub-component is implemented to manage platform data 338, described ingreater detail herein. In various embodiments, the asset repository 471sub-component is implemented to provide access to various cognitivecloud infrastructure assets, such as asset configurations, machineimages, and cognitive insight stack configurations.

In various embodiments, the cloud analytics infrastructure 344 componentmay include a data grid 472 sub-component, a distributed compute engine474 sub-component, and a compute cluster management 476 sub-component.In these embodiments, the cloud analytics infrastructure 344 componentmay also include a distributed object storage 478 sub-component, adistributed full text search 480 sub-component, a document database 482sub-component, a graph database 484 sub-component, and various othersub-components. In various embodiments, the data grid 472 sub-componentis implemented to provide distributed and shared memory that allows thesharing of objects across various data structures. One example of a datagrid 472 sub-component is Redis, an open-source, networked, in-memory,key-value data store, with optional durability, written in ANSI C. Invarious embodiments, the distributed compute engine 474 sub-component isimplemented to allow the cognitive platform 310 to perform variouscognitive insight operations and processes in a distributed computingenvironment. Examples of such cognitive insight operations and processesinclude batch operations and streaming analytics processes.

In various embodiments, the compute cluster management 476 sub-componentis implemented to manage various computing resources as a computecluster. One such example of such a compute cluster management 476sub-component is Mesos/Nimbus, a cluster management platform thatmanages distributed hardware resources into a single pool of resourcesthat can be used by application frameworks to efficiently manageworkload distribution for both batch jobs and long-running services. Invarious embodiments, the distributed object storage 478 sub-component isimplemented to manage the physical storage and retrieval of distributedobjects (e.g., binary file, image, text, etc.) in a cloud environment.Examples of a distributed object storage 478 sub-component includeAmazon S3®, available from Amazon.com of Seattle, Wash., and Swift, anopen source, scalable and redundant storage system.

In various embodiments, the distributed full text search 480sub-component is implemented to perform various full text searchoperations familiar to those of skill in the art within a cloudenvironment. In various embodiments, the document database 482sub-component is implemented to manage the physical storage andretrieval of structured data in a cloud environment. Examples of suchstructured data include social, public, private, and device data, asdescribed in greater detail herein. In certain embodiments, thestructured data includes data that is implemented in the JavaScriptObject Notation (JSON) format. One example of a document database 482sub-component is Mongo, an open source cross-platform document-orienteddatabase. In various embodiments, the graph database 484 sub-componentis implemented to manage the physical storage and retrieval of cognitivegraphs. One example of a graph database 484 sub-component is GraphDB, anopen source graph database familiar to those of skill in the art.

FIG. 5 is a simplified process diagram of cognitive inference andlearning system (CILS) operations performed in accordance with anembodiment of the invention. In various embodiments, these CILSoperations may include a perceive 506 phase, a relate 508 phase, anoperate 510 phase, a process and execute 512 phase, and a learn 514phase. In these and other embodiments, the CILS 117 shown in FIG. 2 isimplemented to mimic cognitive processes associated with the humanbrain. In various embodiments, the CILS operations are performed throughthe implementation of a cognitive platform 310, described in greaterdetail herein. In these and other embodiments, the cognitive platform310 may be implemented within a cloud analytics infrastructure 344,which in turn is implemented within a cloud infrastructure 340, likewisedescribed in greater detail herein.

In various embodiments, multi-site, multi-structured source streams 504are provided by sourcing agents, as described in greater detail herein.In these embodiments, the source streams 504 are dynamically ingested inreal-time during the perceive 506 phase, and based upon a predeterminedcontext, extraction, parsing, and tagging operations are performed onlanguage, text and images contained in the source streams 504. Automaticfeature extraction and modeling operations are then performed with thepreviously processed source streams 504 during the relate 508 phase togenerate queries to identify related data (i.e., corpus expansion).

In various embodiments, operations are performed during the operate 510phase to discover, summarize and prioritize various concepts, which arein turn used to generate actionable recommendations and notificationsassociated with predetermined plan-based optimization goals. Theresulting actionable recommendations and notifications are thenprocessed during the process and execute 512 phase to provide cognitiveinsights, such as recommendations, to various predetermined destinationsand associated application programming interfaces (APIs) 524.

In various embodiments, features from newly-observed data areautomatically extracted from user feedback during the learn 514 phase toimprove various analytical models. In these embodiments, the learn 514phase includes feedback on observations generated during the relate 508phase, which is provided to the perceive 506 phase. Likewise, feedbackon decisions resulting from operations performed during the operate 510phase, and feedback on results resulting from operations performedduring the process and execute 512 phase, are also provided to theperceive 506 phase.

In various embodiments, user interactions result from operationsperformed during the process and execute 512 phase. In theseembodiments, data associated with the user interactions are provided tothe perceive 506 phase as unfolding interactions 522, which includeevents that occur external to the CILS operations described in greaterdetail herein. As an example, a first query from a user may be submittedto the CILS system, which in turn generates a first cognitive insight,which is then provided to the user. In response, the user may respond byproviding a first response, or perhaps a second query, either of whichis provided in the same context as the first query. The CILS receivesthe first response or second query, performs various CILS operations,and provides the user a second cognitive insight. As before, the usermay respond with a second response or a third query, again in thecontext of the first query. Once again, the CILS performs various CILSoperations and provides the user a third cognitive insight, and soforth. In this example, the provision of cognitive insights to the user,and their various associated responses, results in unfoldinginteractions 522, which in turn result in a stateful dialog that evolvesover time. Skilled practitioners of the art will likewise realize thatsuch unfolding interactions 522, occur outside of the CILS operationsperformed by the cognitive platform 310.

FIG. 6 depicts the lifecycle of CILS agents implemented in accordancewith an embodiment of the invention to perform CILS operations. Invarious embodiments, the CILS agents lifecycle 602 may includeimplementation of a sourcing 317 agent, an enrichment 425 agent, abridging 429 agent, an insight 433 agent, a destination 336 agent, and alearning 434 agent. In these embodiments, the sourcing 317 agent isimplemented to source a variety of multi-site, multi-structured sourcestreams of data described in greater detail herein. These sourced datastreams are then provided to an enrichment 425 agent, which then invokesan enrichment component to perform enrichment operations to generateenriched data streams, likewise described in greater detail herein.

The enriched data streams are then provided to a bridging 429 agent,which is used to perform bridging operations described in greater detailherein. In turn, the results of the bridging operations are provided toan insight 433 agent, which is implemented as described in greaterdetail herein to create a visual data story, highlighting user-specificinsights, relationships and recommendations. The resulting visual datastory is then provided to a destination 336 agent, which is implementedto publish cognitive insights to a consumer of cognitive insight data,likewise as described in greater detail herein. In response, theconsumer of cognitive insight data provides feedback to a learning 434agent, which is implemented as described in greater detail herein toprovide the feedback to the sourcing agent 317, at which point the CILSagents lifecycle 602 is continued. From the foregoing, skilledpractitioners of the art will recognize that each iteration of thecognitive agents lifecycle 602 provides more informed cognitiveinsights.

FIG. 7 is a simplified block diagram of a universal knowledge repositoryenvironment 700 implemented in accordance with an embodiment of theinvention to perform Cognitive Inference and Learning System (CILS)operations. As used herein, a universal knowledge repository broadlyrefers to a collection of knowledge elements that can be used in variousembodiments to generate one or more cognitive insights described ingreater detail herein. In various embodiments, these knowledge elementsmay include facts (e.g., milk is a dairy product), information (e.g., ananswer to a question), descriptions (e.g., the color of an automobile),skills (e.g., the ability to install plumbing fixtures), and otherclasses of knowledge familiar to those of skill in the art. In theseembodiments, the knowledge elements may be explicit or implicit. As anexample, the fact that water freezes at zero degrees centigrade would bean explicit knowledge element, while the fact that an automobilemechanic knows how to repair an automobile would be an implicitknowledge element.

In this embodiment, a sourcing agent 704 is implemented by a CILS 700 toperform data sourcing operations, as described in greater detail herein,to source data from an external data source 702, which is one of aplurality of data sources on which data sourcing operations areperformed. In various embodiments, the data sourced from an externaldata source may include social data, public data, licensed data,proprietary data, or any combination thereof. Mapping processes 706,likewise described in greater detail herein, are then performed on thesourced data, followed by a determination being made in decision block710 whether the sourced data contains text. In certain embodiments, aquery 708, such as a user query, is received by the CILS 700, likewisefollowed by a determination being made in decision block 710 whether thequery 708 contains text.

If it is determined in decision block 710 that the sourced data or query708 contains text, then Natural Language Processing (NLP) 712operations, described in greater detail herein, are respectivelyperformed on the sourced data or query 708. In certain embodiments, thenatural language processes 712 may include parsing and resolutionoperations familiar to those of skill in the art. The results of the NLP712 operations are then provided to the knowledge universe 714 forfurther processing. However, if it was determined in decision block 710that the sourced data or received query 708 does not contain text, thenit is likewise provided to the knowledge universe 714 for furtherprocessing. The parsed, or non-parsed, data is then stored in theuniversal knowledge repository 716 as knowledge elements.

As shown in FIG. 7, the knowledge universe 714 includes a universalknowledge repository 716, which is implemented in various embodimentswith rule sets 718 to process the results of NLP 712 operations, oralternatively, sourced data and queries 708 that do not contain text. Inthese embodiments, the rule sets 718 are used by the CILS 700 incombination with the universal knowledge repository 716 to performdeduction and inference operations described in greater detail herein.In certain embodiments, the rule sets are implemented with machinelearning processes 720, familiar to skilled practitioners of the art, toperform the deduction and inference operations. In various embodiments,one or more components of the universal knowledge repository environment700 combine to provide an ingestion pipeline via which information isingested into (i.e., processed for stored storage within) the universalknowledge repository 716. In certain embodiments, the ingestion pipelineperforms parsing operations, machine learning operations andconceptualization operations. In various embodiments, the parsingoperations produces a plurality of parse trees, the machine learningoperations resolve which knowledge elements within the parse treeprovide a best result representing a meaning of the text, the machinelearning operation identifying knowledge elements of the plurality ofparse trees (i.e., a parse forest) representing ambiguous portions ofthe text, and the conceptualization operations identify relationships ofconcepts identified from within the plurality of parse trees producedvia the parsing operation and store knowledge elements within thecognitive graph in a configuration representing the relationship of theconcepts. In certain embodiments, the conceptualization operationidentifies the relationships of the concepts using information storedwithin the cognitive graph. In certain embodiments, the machine learningoperations resolve the plurality of parse trees to a best treerepresenting an interpretation of the ambiguous portions of the text. Incertain embodiments, the interpretation of the ambiguous portions oftext may change based upon feedback.

In various embodiments, the universal knowledge repository 716 isimplemented in combination with an enrichment agent 722 to performenrichment operations, described in greater detail herein. In certainembodiments, the results of the machine learning, enrichment, ordeduction and learning operations, or a combination thereof, are thenstored in the universal knowledge repository 716 as knowledge elements.In various embodiments, the universal knowledge repository 716 isimplemented with an insight agent 724 (such as the insight agent 433)for the generation of cognitive insights (including composite insights),likewise described in greater detail herein. In certain embodiments, theinsight agent 724 accesses a plurality of query related knowledgeelements, processes a query related insight to identify a meaning of aquery from a user and accesses a plurality of answer related knowledgeelements. In certain embodiments, the insight agent 724 traverses aplurality of answer related knowledge elements within the cognitivegraph, the traversing being based upon the meaning of the query inferredby the insight agent 724. In certain embodiments, the traversingcomprises the insight agent 724 accessing nodes of interest (i.e., nodesrelating to a particular query based upon concepts identified by thenodes) with greater and greater detail based upon nodes related viaedges.

In various embodiments, the knowledge elements within a universalknowledge repository 716 may include statements, assertions, beliefs,perceptions, preferences, sentiments, attitudes or opinions associatedwith a person or a group. As an example, user ‘A’ may prefer the pizzaserved by a first restaurant, while user ‘B’ may prefer the pizza servedby a second restaurant. Furthermore, both user ‘A’ and ‘B’ may be firmlyof the opinion that the first and second restaurants respectively servethe very best pizza available. In this example, the respectivepreferences and opinions of users ‘A’ and ‘B’ regarding the first andsecond restaurant may be included in the universal knowledge repository716 as they are not contradictory. Instead, they are simply knowledgeelements respectively associated with the two users and can be used invarious embodiments for the generation of various cognitive insights, asdescribed in greater detail herein.

In certain embodiments, knowledge elements, are persistently storedwithin the universal knowledge repository (i.e., once stored within auniversal knowledge repository 716, each knowledge element is notdeleted, overwritten or modified), Knowledge elements are persisted intheir original form (i.e., with all knowledge elements once stored, theoriginal form of the knowledge element is continuously maintained withinthe universal graph). As an example, a first knowledge element (e.g., astatement or assertion) sourced from a first source and with anassociated first time stamp may be contradicted by a second knowledgeelement sourced from a second source and associated with a second timestamp. As another example, a first knowledge element may be contradictedby a second knowledge element sourced from the same source, but withdifferent associated time stamps. As yet another example, a firstknowledge element may be contradicted by a second knowledge elementsourced respectively from a first source and a second source, but theirassociated time stamps may be the same, or approximate thereto. As yetstill another example, a first knowledge element may be found to beincorrect as a result of the addition of a second knowledge elementrelated to the same subject. Those of skill in the art will recognizethat many such embodiments and examples are possible and the foregoingis not intended to limit the spirit, scope or intent of the invention.

In various embodiments, knowledge is universally represented within theuniversal knowledge repository 716 such that the knowledge can beuniversally accessed regardless of a knowledge domain associated withthe knowledge. As used herein, universally represented broadly refers toa faithful representation of the knowledge such that the knowledge isrepeatably accessible within the universal knowledge repository,regardless of the actual location where the knowledge is stored. Inthese embodiments, information is first normalized to a common model ofrepresentation to allow deductions and correlations to be made betweendisparate data sets. This normalized model, which is based upon naturallanguage rather than an arbitrarily defined schema, allows bothstructured and unstructured data to be understood in the same way byproviding a non-arbitrary, universally recognizable schema.

In particular, this approach to cognitive knowledge representation(i.e., where knowledge is universally represented in both accessibilityand domain) is domain-nonspecific, or encompassing of all domainsuniversally, because language itself expresses concepts cross-domain. Invarious embodiments, domains are used to aid in resolution. As anexample, “planes of existence” are less likely to be discussed in atravel context than are “airplanes,” but both are still possibleinterpretations of the word “plane.” Such an approach allows resolutionto be descriptive rather than rule-based. To continue the example, if amachine learning algorithm detects a change, moving from “airplane” to“plane of existence” as the predominant likely interpretation, changescan be made without manual reprogramming.

As another example, a travel company may not have the concept for a“Jazzercise unitard” within their business domain model, but a consumercould potentially ask “What's the best place to buy a jazzercise unitardin San Diego while I'm visiting on vacation?” In this example, both thetravel and retail domains are invoked and almost any combination isconceivable. With traditional search approaches, the consumer isinstructed not to cross the two domains, which would likely result ininaccurate or non-relevant results. In contrast, the consumer can askwhatever they wish in a cognitive query and receive more accurate orrelevant search results through the implementation of the universalknowledge repository 716.

In various embodiments, the CILS 700 processes queries 708 nodifferently than answer data, aside from the response-prompt. In theseembodiments, both queries and answers to queries are stored in theuniversal knowledge repository 716 in the same way (i.e., via aconsistent, non-arbitrary, universally recognizable schema). Queries arestored as query related knowledge elements and answers are stored asanswer related knowledge elements. This storage is effectivelyconceptualization or “understanding” of the data, as both the questionand the answer need to be equally understood. As an example, if a userinputs the following query 708, their reference can be handled as eithera question or as an answer to a question:

“Are electric guitars cool?”

-   -   ->“Obviously.”    -   ->“You're the 5th person to ask that question. I guess so.”

In various embodiments, the universal knowledge repository 716 isimplemented as a universal cognitive graph, with each concept itcontains having its own representation, from classes, to types, toinstance data. In these embodiments, there is no schematic distinctionbetween a class and an instance of that class. As an example, “patient”and “patient #12345” are both stored in the graph, and may be accessedthrough the same traversals. In various embodiments, the universalcognitive graphs contain a plurality of knowledge elements which arestored as nodes within the graph and subsets of nodes (e.g., pairs ofnodes) are related via edges.

FIGS. 8a through 8c are a simplified process flow diagram of theperformance of operations related to the use of a universal knowledgerepository implemented in accordance with an embodiment of the inventionby a Cognitive Inference and Learning System (CILS) for the generationof cognitive insights. In various embodiments, a query such as a naturallanguage query 802 is received by a CILS 800. In certain embodiments,the natural language query may be originated by a user 842.

In various embodiments, an input source data query 804 is likewisereceived by a CILS 800. In certain embodiments, the input source dataquery 804 uses data received from an external data source 844, describedin greater detail herein. In various embodiments, the external datasource 844 is administered by a system developer 846. In theseembodiments, a mapper rule set 806, described in greater detail herein,is used to perform mapping operations (such as Mapping processes) on theinput source data query 804. In certain embodiments, the mapper rule set806 is administered by a user 848, such as an application developer.

Parsing operations 808, described in greater detail herein, are thenperformed on the natural language query 802, or the input source dataquery 804, once the mapper rule set 806 has been used to perform themapping operations. In various embodiments, the parsing operation 808comprises lossless parsing operations. As used herein, a losslessparsing operation may be defined as a parsing operation in which allparse variations are identified using a minimum basic structure that isknown true. In certain embodiments, the lossless parsing operation usesa baseline generalized grammar via which all language can be parsed. Incertain embodiments, machine learning is applied to the all possibleknown true parse variations to identify ambiguities and separate outerror possibilities.

In various embodiments, a parse rule set 810 is used to perform theparsing processes 808. In certain embodiments, the parse rule set 810 isadministered by a system developer 846. In certain embodiments, theparse rule set 810 is administered by a linguist 850.

The use of the parse rule set 810 in the performance of the parsingprocesses 808 results in the generation of a set of parse options 816(i.e., a set of parse trees (i.e., a parse forest)). In certainembodiments, each parse tree represents an alternate parse of the textbeing parsed. A parse ranking rule set 818 is then used to rank theresulting set of parse options 816, which results in the determinationof the top-ranked parse options 820 (i.e. highly-ranked parse treesbased upon the parse ranking rule set). In various embodiments, theparse ranking rule set 818 is automatically instantiated through the useof machine learning processes 854 familiar to those of skill in the art,manually instantiated by a linguist 850, or some combination thereof. Inone embodiment, the parse ranking rule set 818 is administered by alinguist 850. In another embodiment, the machine learning processes 854are administered by a system developer 841. In certain embodiments, theCorpus of Contemporary American English (COCA), other training corporafamiliar to skilled practitioner of the art, or some combinationthereof, is used as a reference to train the machine learning processes854. In various embodiments, the parse ranking rule set ranks which ofthe parse trees are likely to provide a preferred (e.g., correct)resolution of the parsing operation. In various embodiments, the parseranking rule set 818 includes sets of rules for each parsed elementbased upon what is trustworthy and what is not trustworthy. In variousembodiments, if a parsing of data is incorrect, then learning (e.g.,machine learning) is applied over time to adjust the parse ranking ruleset to provide a more accurate parse. In certain embodiments, thelearning is based upon feedback provided over time regarding the parsingof the data. In certain embodiments, machine learning is used to rankparse variations within the parse ranking rule set. In certainembodiments, the machine learning uses feedback to produce a labeled setof parse options. In certain embodiments, context is used to resolveambiguity and to adjust a resolution of the parse (i.e., to adjust theranking of a parse within the parse rule set).

Conceptualization processes 822 (which may comprise conceptualizationoperations), described in greater detail herein, are then performed onthe top-ranked parse option results 820. In various embodiments, theconceptualization processes 822 are performed through the use of aconceptualization rule set 824. In certain embodiments, theconceptualization rule set 824 is manually instantiated and administeredby a system developer 846, a linguist 850, or some combination thereof.

The use of the conceptualization rule set 824 in the performance of theconceptualization operations 822 results in the generation of a set ofconceptualization ambiguity options 826. A conceptualization rankingrule set 828 is then used to rank the resulting set of conceptualizationambiguity options 826, which results in the determination of thetop-ranked conceptualization options 830 (i.e., highly ranked based uponapplication of the conceptualization ranking rule set). In variousembodiments, the conceptualization ranking rule set 828 is automaticallyinstantiated through the use of machine learning processes 854, manuallyinstantiated by a linguist 850, or some combination thereof. In oneembodiment, the conceptualization ranking rule set 828 is administeredby a linguist 850. The top-ranked conceptualization options 830 are thenstored in the universal knowledge repository 832. In variousembodiments, the universal knowledge repository 816 is implemented as auniversal cognitive graph. In certain embodiments, the universalknowledge repository 832 is administered by a linguist.

In various embodiments, an insight agent 834 generates an insight agentquery 836, which is then submitted to the universal knowledge repository832 for processing. In certain embodiments, a matching rule set 838 isused to process the insight agent query 836, which 834 results in thegeneration of matching results 838. In one embodiment, the matching ruleset is administered by a linguist 850. The matching results are thenprovided back to the insight agent 834. In one embodiment, the insightagent query 836 is generated as a result of direct or indirect input,described in greater detail herein, by a user 842. In anotherembodiment, the insight agent is administered by an applicationdeveloper 848.

FIG. 9 is a simplified depiction of an example universal schemaimplemented in accordance with an embodiment of the invention. Skilledpractitioners of the art will be aware that natural language is anexpression of cognition, following syntactic rules of a cultural grammaras well as logical rules of entailment. As used herein, entailmentbroadly refers to the concept of understanding language, within thecontext of one piece of information being related to another. Forexample, if a statement is made that implies ‘x’, and ‘x is known toimply ‘y’, then by extension, the statement may imply ‘y’ as well. Inthis example, there is a chaining of evidence between the statement ‘x’and ‘y’ that may result in a conclusion supported by the chain ofevidence. As another example, based upon the study of philosophy, thestatement that Socrates is a person, and all people are mortal, then theimplication is that Socrates is mortal.

In various embodiments, a universal knowledge repository is implementedwith an ontology that is based upon entities, which are anything that“is,” and their attributes, which are anything that is “had.” As usedherein, an ontology may be defined as a representation of entities alongwith their properties and relations, according to a system ofcategories. In certain embodiments, the ontology universally representsknowledge where knowledge elements are structurally defined within thecognitive graph. In certain embodiments, two types of entities areimplemented to represent all relationships within the universalknowledge repository through a categorical relationship (e.g., is a) andan attributive relationship (e.g., has a). This approach allowssentences to be rephrased in terms of “be” or “have” and either can beconverted into the other. Accordingly, sentences can be rephrased toaccommodate various permutations of whatever is present within theuniversal knowledge repository. In certain embodiments, a categoricalrelationship may be considered as inheritance of the entity and anattributive relationship may be considered as attribution of the entity.

In various embodiments, the universal knowledge repository isimplemented as a universal cognitive graph. In certain of theseembodiments, the universal cognitive graph may include an entailmentgraph, which not only provides a representation of set and typetheories, but also models knowledge through inheritance. At any givennode in the graph, upward relationships identify entailed concepts. Itwill be appreciated that one advantage of this approach is the abilityto harness the simplest possible generalization of pathways within theuniversal cognitive graph to answer questions rather than hard-codingvarious question-and-answer pairs.

In various embodiments, every item within the universal knowledgerepository ontology is a knowledge entity. More particularly, anythingthat “is,” in any domain, whether real or unreal, is an entity, and anyentity that is “had,” is also an attribute. This includes both types andinstances, such as:

-   -   unicorn    -   clinical study    -   patient    -   attribute of a patient    -   patient #67432

In these embodiments, an inheritance relationship implies a tacit “canbe” relationship in the other direction. For example, all persons areanimals, and an animal “could be” a person if it meets the definition ofperson. Likewise, a child node in the universal cognitive graph inheritsall the attributes of its parents and can further specify its own. Aconcept, like a type in type theory, represents a set of attributes. Iftwo sets of attributes are not identical, then each represents a uniqueconcept. Conversely, if two sets of attributes are identical, then theyare represented by the same concept.

In various embodiments, entities are represented as nodes in a universalcognitive graph, and their corresponding attribute and inheritancerelationships with other nodes are represented as graph edges. Forexample, as shown in FIG. 9, a “patient” node 930 is an entity thatinherits from a “person” node 920, which in turn is an entity thatinherits from an “animal” node 902. As likewise shown in FIG. 9, theinheritance relationships corresponding to nodes 930, 920, and 902 arerespectively represented by “is a” graph edges.

Likewise, the “animal” node 902 has an attribute relationship with the“attribute of animal” node 904, the “person” node has an attributerelationship with the “attribute of person” node 922, and the “patient”node 930 has an attribute relationship with the “attribute of patient”node 932. As likewise shown in FIG. 9, the attribute relationshipsbetween nodes 902 and 904, nodes 920 and 922, and nodes 930 and 932, arerespectively represented by a “has a” graph edge, and the inheritancerelationships between nodes 904, 922, and 932 are respectivelyrepresented by “is a” graph edges.

To continue the example, the “size of animal” node 906 has aninheritance relationship with the “attribute of animal” node 904,represented by a “is a” graph edge. Furthermore, the “weight of animal”node 908, the “length of animal” node 910, and the “height of animal”node 912 all have an inheritance relationship with the “size of animal”node 906, each of which are respectively represented by a corresponding“is a” graph edge. Likewise, the “where animal lives” node 914, the“where person lives” node 924, and the “where patient lives” node 934respectively have an inheritance relationship with the “attribute ofanimal” node 904, “attribute of person” node 922, and the “attribute ofpatient” node 932. As shown in FIG. 9, each of these inheritancerelationships are respectively represented by a corresponding “is a”graph edge.

Continuing the example, the “animal habitat” node 916 and the “animalresidence” node 917 both have an inheritance relationship with the“where animal lives” node 914. Likewise, the “person habitat” node 926and the “home” node 928 both have an inheritance relationship with the“where person lives” node 924, and the “patient habitat” node 936 andthe “patient's home” node 938 both have an inheritance relationship withthe “where patient lives” node 934. In addition, the “patient's home”node 938 has an inheritance relationship with the “home” node 928, whichin turn has an inheritance relationship with the “animal residence” node917. Likewise, the “patient habitat” node 936 has an inheritancerelationship with the “person habitat” node 926, which in turn has aninheritance relationship with the “animal habitat” node 916. As shown inFIG. 9, these inheritance relationships are respectively represented bya corresponding “is a” graph edge.

From the foregoing, skilled practitioners of the art will recognize thatif the “patient” node 930 of the universal cognitive graph depicted inFIG. 9 is queried, then both the “person” node 920 and the “animal” node902 are entailed due to their respective inheritance relationships.Furthermore, the “person” node 920 does not have attribute relationshipswith individual attribute nodes, such as the “home” node 928. Instead,the “person” node 920 has an attribute relationship with the “attributeof person” node 922, which in turn has direct and indirect relationshipswith other attribute nodes (e.g., nodes 924, 826, 928), which representattributes that are indigenous to a person.

FIG. 10 depicts the use of diamond and ladder entailment patternsimplemented in accordance with an embodiment of the invention toaccurately and precisely model knowledge elements in a universalcognitive graph. As used herein, a diamond entailment pattern broadlyrefers to a pattern formed in a universal cognitive graph when two ormore parent nodes of a first node inherit from a common second node. Forexample, as shown in FIG. 10, the “human body part” node 1004 inheritsfrom the “body part” node 1002. In turn, both the “human male body part”node 1006 and the “human femur” node 1008 inherit from the “human bodypart” node 1004. Likewise, the “human male femur” node 1010 inheritsfrom both the “human male body part” node 1006 and the “human femur”node 1008.

As another example, both the “male” node 1014 and the “human” node 1016inherit from the “life form” node 1012. Likewise, the “human male” node1018 inherits from both the “male” node 1014 and the “human” node 1008.As a result, nodes 1004, 1006, 1008, and 1010 form a diamond entailmentpattern, as do nodes 1012, 1014, 1016 and 0018. Accordingly, theinheritance relationships between the base node 1010 and both of itsparents 1006 and 1008 define a particular concept, meaning that any nodethat matches both the “human male body part” node 1006 and the “humanfemur” node 1008 matches the requirements for the “human male femur”node 1010. Likewise, the inheritance relationships between the base node1018 and both of its parents 1014 and 1016 define another concept,meaning that any node that matches both the “male” node 1014 and the“human” node 1016 matches the requirements for the “human male” node1018.

Skilled practitioners of the art will recognize that the implementationof such intermediate nodes (e.g., nodes 1006, 1008, 1014, 1016, etc.)allow an accurate and precise representation of knowledge within auniversal cognitive graph. For example, the “human male” node 1018 doesnot have a direct relationship to the “body part” node 1002 because notall body parts are had by humans, and conversely, not all body parts arehad by human males. To continue the example, some body parts are had byplants, like stamens, some are had by birds, like feathers, and some arehad by fish, like gills, and so forth.

As shown in FIG. 10, the uniqueness of these relationships can beaccurately and precisely modeled through the implementation of a ladderentailment pattern. As used herein, a ladder entailment pattern broadlyrefers to a pattern formed in a universal cognitive graph where therungs of the ladder entailment pattern are formed by a first set ofnodes having a “has a” relationship with a corresponding second set ofnodes and the rails of the ladder entailment pattern are formed by “isa” relationships respectively associated with the first and second setof nodes.

For example, the “body part” node 1002 is directly defined by a “has a”relationship to the “life form” node 1012, which forms a rung of aladder entailment pattern. Likewise, the “human body part” node 1004 andthe “human male body part” node 1006 are respectively defined bycorresponding “has a” relationships to the “human” node 1016 and the“human male” node 1018, which form additional rungs of a ladderentailment pattern. To continue the example, a first rail of the ladderentailment pattern is formed by the “is a” relationships between the“human male” node 1018, the “human” node 1016 and the “life form” node1012. Likewise, a second rail of the ladder entailment pattern is formedby the “is a” relationships between the “human male body part” node1006, the “human body part” node 1004, and the “body part” node 1002.

As another example, the “plant body part” node 1022, the “bird bodypart” node 1028, and the “fish body part” node 1034 are directly andrespectively defined by a “has a” relationship to the “plant” node 1020,the “bird” node 1026, and the “fish” node 1032. In this example, therespective “has a” relationships form corresponding rungs of separateladder entailment patterns. To continue the example, a first rail ofthese separate ladder entailment patterns is formed by the respective“is a” relationships between the “plant” node 1020, the “bird” node1026, the “fish” node 1032 and the “life form” node 1012. Likewise, asecond rail of these separate ladder entailment patterns is formed bythe respective “is a” relationships between the “plant body part” node1022, the “bird body part” node 1028, the “fish body part” node 1034 andthe “body part” node 1002.

As yet another example, a human named John Doe may be represented by a“John Doe” node 1038, which is defined by a “is a” relationship with the“human male” node 1018. To continue the example, the “John Doe” node1038 may have a corresponding “has a” relationship with a “John Doe'sbody part” node 1040, which is likewise defined by a “is a” relationshipwith the “human male body part” node 1006. In this example, the firstrail of the ladder entailment pattern formed by the “is a” relationshipsbetween the “human male” node 1018, the “human” node 1016 and the “lifeform” node 1012, and is extended by the addition of the “is a”relationship between the “John Doe” node 1038 and the “human male” node1018. Likewise, the second rail of the ladder entailment pattern isformed by the “is a” relationships between the “human male body part”node 1006, the “human body part” node 1004, and the “body part” node1002, and is extended by the addition of the “is a” relationship betweenthe “John Doe's body part” node 1040 and the “human male body part” node1006. An additional rung is likewise formed in this ladder entailmentpattern by the “has a” relationship between the “John Doe” node 1038 andthe “John Doe's body part” node 1040.

In various embodiments, the creation of a first node (e.g., the “JohnDoe” node 1038) results in the creation of a second, complementary node(e.g., the “John Doe's body part” node 1040. In these embodiments,associated “is a” and “has a” relationships are likewise created in theuniversal cognitive graph to maintain a ladder entailment pattern. Inone embodiment, the creation of the second, complementary node mayresult in the creation of a third node that contains associatedinformation. In this embodiment, the second, complementary node may beassociated with the third node by the addition of either a “has a” or“is a” relationship. In another embodiment, the creation of the second,complementary node may result in the addition of either a “has a” or “isa” relationship with an existing third node in the universal cognitivegraph.

In yet another embodiment, creation of the second, complementary nodemay result in the generation of a request for additional informationassociated with the second, complementary node. For example, thecreation of the “John Doe” node 1038 may result in the creation of the“John Doe's body part” node 1040. In this example, a request may begenerated requesting more information related to John Doe's body parts.The response to the request may in turn result in the creation of a“John Doe's femur” node (not shown). To continue this example, thecreation of the “John Doe's femur” node would result in the creation ofan additional diamond pattern, as the “John Doe's femur node wouldinherit from both the “John Doe's body part” node 1040 and the “humanmale femur” node 1010, both of which inherit from the “human male bodypart” node 1006.

Using this approach, all life forms can be represented as having bodyparts, and conversely, all body parts can be represented as being of, orfrom, a life form. As a result, when a body part is identified by aCognitive Inference and Learning System (CILS), a life form is entailed.Likewise, when a life form is identified by the CILS, the body part ofthat life form is entailed. For example, the “stamen” node 1024, the“feather” node 1030, and the “gill” node 1036 are respectively definedby the “plant body part” node 1022, the “bird body part” node 1028, the“fish body part” node 1034 and the “body part” node 1002. Accordingly, aconcept such as a human femur is separated from concepts such as gills,feathers and stamens, none of which are had by humans as part of theirbodies. Those of skill in the art will realize that many such examplesand embodiments are possible and foregoing is not intended to limit thespirit, scope or intent of the invention.

FIGS. 11a through 11d are a simplified graphical representation ofquantity modeled as knowledge elements in a universal cognitive graphimplemented in accordance with an embodiment of the invention. Invarious embodiments, entities are represented by individual nodes in anentailment graph, such as the universal cognitive graph shown in FIGS.11a through 11d . In these embodiments, each class of entitiesrepresents any single instance of that entity. Because any instance ofan entity is exactly one thing, the quantity referenced by the class is“exactly one.”

For example, as described in greater detail herein, a “dog” entityrepresents all valid attributes of any single instance of a dog throughthe implementation of “has a” relationships. Accordingly, the “dog”class does not refer to all dogs, nor does it refer to at least one dog.To continue the example, the set of all dogs might be dangerous in aconfrontational situation, while the single dog “Skipper” could beharmless while playing catch. Accordingly, attributes of a set arenecessarily distinct concepts from attributes of any single instance ofthat set.

As such, expressions for “greater than one entity” are represented invarious embodiments as a set. For example, a “group” may be defined asany instance of exactly one set which contains 2 or more elements. As anillustration of this example:

“Three people walked into the room.”

-   -   ->“2 people walked into a room”    -   ->“4 people walked into a room” cannot be ascertained    -   ->“Only three people walked into a room.”    -   ->“2 people walked into a room.”    -   ! ->“12 people walked into a room.”

“One person walked into a room.”

As another example, something that is true of a group of things together(e.g., the engineering department is large) is not necessarily true ofthe members themselves (e.g., each engineer is petite), as demonstratedin the preceding example. In various embodiments, this approach allowsaccurate conceptualization of complicated quantifiers and markers suchas “only”, “any”, “all”, and “not.” As used herein, “marker” refers tolinguistic markers that identify quantity. Other examples of suchmarkers include “most,” “some, “few,” “none,” and so forth. In certainembodiments, numeric quantifiers (e.g., ‘5’, ‘6’, ‘7’, etc.) areprocessed as markers.

For example, the sentence “One person walked into a room.” refers to aninstance of one person, and does not preclude the possibility that twopeople walked into a room. This same sentence, however, does precludethe possibility that two people walked into the room. Furthermore, italso gives connotation to the notion that there could have been morepeople, or perhaps more people were expected.

As yet another example, the sentence “Only one person walked into aroom.” is not referring to a particular person, but rather to any setthat contains exactly one person. As yet still another example, thesentence “One person acting alone could potentially get in undetected;admitting three people unnoticed, however, is beyond belief.” uses a“two is better than one” construction, which provides a way to accessthe class rather than an instance. It will be appreciated that thecomplexity of modeling quantity as knowledge elements in a universalcognitive graph is further compounded in the case of zero quantities, asthey are conceptually very similar to negation, which reverses normalentailment patterns. For example, the sentence, “Not even one person isa member of the set of things that walked into a room.” references theset of less than one person, zero people, no people, and nothing.

Accordingly, these approaches to modeling quantity as knowledge elementsin a universal cognitive graph can be used when processing the sentence,“Sue, Bob and Larry walked into a room.” As shown in FIGS. 11a through11d , the “set of people” node 1108 is defined by the “set” node” 1104,which in turn is defined by the “entity” node 1102, which refers to anyentity that is exactly the numeric value of one. Likewise, the “3people” node 1130 is defined by the “3 entities” node 1132 and the “2people” node 1122, which is defined by the “2 entities” node 1124 andthe “1 person” node 1116, which in turn is defined by the “1 entity”node 1112 and the “set of people” node 1108. The “3 entities” node 1132is likewise defined by the “2 entities” node 1124, which is defined bythe “1 entity” node 1112 and the “group” node 1110, both of which are inturn defined by the “set” node 1104.

As likewise shown in FIGS. 11a through 11d , the “3 entities” node 1132,the “2 entities” node 1124, the “1 entity” node 1112, and the “set”node” 1104 respectively have a “has a” relationship with the “member ofset of 3 entities” node 1136, the “member of set of 2 entities” node1128, the “member of set of 1 entity” node 1114, and the “member” node”1106. Likewise, the “3 people” node 1130, the “2 people” node 1122, andthe “1 person” node 1116 respectively have a “has a” relationship withthe “member of set of 3 people” node 1136, the “member of set of 2people” node 1128, and the “member of set of 1 person” node 1114. Inturn, the “member of set of 3 people” node 1134 is defined by the“member of set of 2 people” node 1126 and the “member of set of 3entities” node 1136, both of which are defined by the “member of set of2 entities” node 1128.

The “member of set of 2 people” node 1126 is likewise defined by the“member of set of 1 person” node 1118 and the “member of set of 2entities” node 1128. Likewise, the “member of set of 1 person” node 1118is defined by the “member of set of 1 entity” node 114, which is definedby the “entity” node 1102 and the “member” node 1106, which is likewisedefined by the “entity” node 1102. The “member of set of 3 entities”node 1136 is likewise defined by the “member of set of 2 entities” node1128, which in turn is defined by the “member of set of 1 entity” node1114. Likewise, the “member of set of 3 people” node 1136, the “memberof set of 2 people” node 1126, and the “member of set of 1 person” node1118 are defined by the “person” node 1120, which in turn is defined bythe “entity” node 1102.

Likewise, as shown in FIGS. 11a through 11d , the “member of set of 3people” node 1134, the “member of set of 2 people” node 1126, and the“member of set of 1 person” node 1118 respectively have a “has is”relationship with the “attribute of member of set of 3 people” node1158, the “attribute of member of set of 2 people” node 1154, and the“attribute of member of set of 1 person” node 1152. The “member of setof 3 entities” node 1136, the “member of set of 2 entities” node 1128,and the “member of set of 1 entity” node 1114 likewise respectively havea “has is” relationship with the “attribute of member of set of 3entities” node 1160, the “attribute of member of set of 2 entities” node1156, and the “attribute of member of set of 1 entity” node 1150.

Likewise, the “attribute of member of set of 3 people” node 1158 and the“attribute of member of set of 3 entities” node 1160 is defined by the“attribute of member of set of 2 people” node 1154, both of which are inturn defined by the “attribute of member of set of 2 entities” node1156. The “attribute of member of set of 2 people” node 1154 is likewisedefined by the “attribute of member of set of 1 person” node 1152, whichin turn is defined by the “attribute of member of set of 1 entity” node1150, as is the “attribute of member of set of 2 entities” node 1156.Likewise, the “attribute of member of set of 1 entity” node 1150 isdefined by the “attribute” node 1148, which in turn is defined by the“entity” node 1102.

Numeric quantities are likewise represented in the universal cognitivegraph by the “3” node 1170, the “2” node 1168, the “1” node 1166, andthe “0” node 1164, all of which are defined the “quantity” node 1162,which in turn is defined by the “entity” node 1102. Additionally, the“3” node 1170 is defined by the “member of set of 3 entities” node 1136,the “2” node 1168 is defined by the “attribute of member of set of 2people” node 1154, the “1” node 1166 is defined by the “attribute ofmember of set of 1 entity” node 1150, and the “0” node 1164 is definedby a “not is a” relationship with the “member of set of 1 entity” node1114. As used herein, a “not is a” relationship broadly refers to avariant of an “is a” relationship that is implemented in variousembodiments to signify negation. For example, defining the “0” node 1164with a “not is a” relationship” with the “member of set of 1 entity”node 1114 signifies that the numeric value of ‘0’ is not a member of aset of ‘1’.

Linguistic quantifier modifiers are likewise represented in theuniversal cognitive graph by “exactly” node 1183, the “at least” node1184, the “at most” node 1185, the “more than” node 1186, and the “lessthan” node 1187, all of which are defined by the “quantifier modifier”node 1182, which in turn is defined by the “entity” node 1102. Likewise,an action (e.g., walking, speaking, gesturing, etc.) is represented inthe universal cognitive graph shown in FIG. 11 by the “action” node1180, which is defined by the “entity” node 1102. The act of walking(e.g., a person is walking) is likewise represented by the “walk” node1178, which is defined by the “action” node 1180. The “walk” node 1178likewise has a relationship with the “attribute of walk” node 1172,which forms a rung of a ladder entailment pattern, described in greaterdetail herein. In turn, the “attribute of walk” node 1172 node isdefined by the “attribute” node 1148. Likewise, an agent of the act ofwalking is represented by the “agent of walk” node 1176, which isdefined by an “is a” relationship with the “role of walk” node 1174,which in turn is defined by the “attribute of walk” node 1172.

Referring once again to FIG. 11, the “Sue, Bob and Larry walked into aroom” node 1190 represents the concept of a set of three people thatincludes Sue, Bob and Larry walking into a room. As such, the “Sue, Boband Larry walked into a room” node 1190 is defined by the “walk” node1178 node, and likewise has a “has a” relationship with the “attributesof Sue, Bob and Larry walked into a room” node 1188, which forms therung of a ladder entailment pattern. As shown in FIG. 11, the“attributes of Sue, Bob and Larry walked into a room” node 1188 isdefined by the “attribute of walk” node 1172. As likewise shown in FIG.11, the “has a” relationship between the “walk” node 1178 and the“attribute of walk” node 1172 forms another rung of a ladder entailmentpattern. Likewise, the “is a” relationship between “Sue, Bob and Larrywalked into a room” node 1190 and the “walk” attribute 1178, and the “isa” relationship between the “attributes of Sue, Bob and Larry walkedinto a room” node 1188 and the “attribute of walk” node 1172respectively form the rails of a ladder entailment pattern.

Likewise, the “Sue, Bob and Larry” node 1138 represents the concept of aset of three people that includes Sue, Bob and Larry, which is directlydefined through an “is a” relationships by the “3 people” node 1130, andindirectly through an indirect inheritance relationships with the “setof people” node 1108. In this embodiment, the creation of the “Sue, Boband Larry” node 1138 results in the creation of a “member of Sue, Boband Larry” node 1140 and a corresponding “has a” relationship.”Likewise, the “member of Sue, Bob and Larry” node 1140 is defined by the“member of set of 3 people” node 1134.

As shown in FIGS. 11a through 11d , the creation of the “has a”relationship between the “Sue, Bob and Larry” node 1138 and the “memberof Sue, Bob and Larry” node 1140 results in the formation of anotherrung of a ladder entailment pattern. Likewise, the “is a” relationshipbetween the “Sue, Bob and Larry” node 1138 and the “3 people” node 1130,and the “is a” relationship between the “member of Sue, Bob and Larry”node 1140 and “member of set of 3 people” node 1134, respectively formrails to extend the ladder entailment pattern previously formed by the“has a” relationship between the “3 people” node 1130 and the “member ofset of 3 people” node 1134. As likewise shown in FIGS. 11a through 11d ,the “member of Sue, Bob and Larry” node 1140 is also defined by the“Sue” node 1142, the “Bob” node 1144, and the “Larry” node 1146, each ofwhich are likewise defined by the “person” node” 1120.

The “member of Sue, Bob and Larry” node 1140 is likewise defined by the“agent of Sue, Bob and Larry walked into a room” node 1194. In turn, the“member of Sue, Bob and Larry” node 1140 is defined by the “agent ofwalk” node 1176 and the “role of Sue, Bob and Larry walked into a room”node 1192, which is defined by the by ‘3’ node 1170. Likewise, the “roleof Sue, Bob and Larry walked into a room” node 1192 and the “agent ofwalk” node 1176 are both defined by the “role of walk” node 1174. Asshown in FIGS. 11a through 11d , the respective “is a” inheritancerelationships between the “role of walk” node 1174, the “role of Sue,Bob and Larry walked into a room” node 1192, the “agent of walk” node1176, and the “agent of Sue, Bob and Larry walked into a room” node 1194result in the formation of a diamond entailment pattern, described ingreater detail herein.

Accordingly, skilled practitioners of the art will recognize that theforegoing embodiments allow the sentence, “Sue, Bob and Larry walkedinto a room” to be modeled as knowledge elements in a universalcognitive graph such that the set of people that includes Sue, Bob andLarry are quantified as a set of three people. Furthermore, modelingsuch quantification as knowledge elements in a universal cognitive graphsupports instances where members of the set of people that includes Sue,Bob and Larry may enter a room individually, as a complete group, or asubset thereof. Those of skill in the art will likewise recognize thatmany such embodiments and examples are possible and the foregoing is notintended to limit the spirit, scope or intent of the invention.

FIGS. 12a through 12d are a simplified graphical representation oflocation, time and scale modeled as knowledge elements in a universalcognitive graph implemented in accordance with an embodiment of theinvention. In various embodiments, location is represented as, orreferenced to, a set of points in space. In these embodiments, the setof points may include a single point in space, multiple points in space(e.g., an area or volume), or no points in space. Accordingly, anyphysical thing can be, or identify, a location. By extension, any singlepoint in space in these embodiments may be subdivided into any number ofsmaller points, which in turn may be referenced individually andrecursively.

For example, the following sentences demonstrate recursive subdivisionas specific references in a copular sentence:

“Where in the drawer are they?”

-   -   ->“to the left of the candy bars.”

“Her shoes are in the drawer.”

-   -   ->“The location of her shoes is in the drawer.”

“Your keys are on the table between the flowers and my purse.”

“Her house is on Manor Street, after East Side but before Tom's Market.”

Skilled practitioners of the art will be aware that location is the mostcommonly referenced case of recursive subdivision in most languages,including English. However, it conceptually occurs in all pointreferences across dimensions including location, time and scale, such asthe following example, which includes attributes and adjectives:

“What's the restaurant like?”

-   -   ->“It's very pretty.”    -   “How pretty?        -   ->“Beautiful.”        -   ->“How beautiful?”            -   ->“Gorgeous.”            -   “How gorgeous?”                -   ->“Extremely.”

Those of skill in the art will likewise be aware that time, likelocation and scale, can also be relative, due both to its capacity forinfinite division and a set of relative terms, such as tomorrow,yesterday, now, later, and so forth. Accordingly, time, location, andscale are all describable in various embodiments within a universalcognitive graph through the use of terms of location on a scale or alonga dimension. For example:

“At what point did you realize you wouldn't be getting a raise?

-   -   ->“Yesterday at 2 pm.”

The modeling of knowledge related to time, location and space asknowledge elements in a universal cognitive graph may require the use ofeither explicit time references, implicit time references, or somecombination thereof. In various embodiments, this requirement isaccommodated by time-stamping source data received by a CognitiveLearning System (CILS) prior to it being processed for storage asknowledge elements in a universal cognitive graph. In these embodiments,the method by which the source data is time-stamped is a matter ofdesign choice. In certain embodiments, the time-stamp is provided aspart of the initial structure of a statement. For example, time-stampinga statement received at 3:00 pm, Apr. 15, 2015 provides a time referencethat can be used to identify what “tomorrow” means.

In various embodiments, recursive subdivision approaches known to thoseof skill in the art are implemented to model time, location, and scaleas knowledge elements within a universal cognitive graph. As an example,a location may be modeled as being within the United States of America,in the state of Texas, in the city of Austin, in a building at aparticular address, in a room at that address, underneath a tablelocated in that room, on an item on the floor under the table, and soforth. As another example, a period of time may be referenced as beingwithin the year 2015, in the month of April, on the 15^(th) day, in theafternoon of the 15^(th), at 15:07 pm, at eleven seconds after 15:07,and so forth. To continue the preceding examples, location scale may bereferenced as “somewhere within the room,” and time scale maybereferenced as “sometime after 15:07 pm.”

In certain embodiments, recursive subdivision is implemented in themodeling of location, time and scale as knowledge elements in auniversal cognitive graph to accurately maintain transitivity. As anexample, a house may have a door, and the door may have a handle. Inthis example, the statement, “The house has a handle.” would beaccurate. To continue the example, the house may also have a teapot,which likewise has a handle. However, since the teapot is not physicallypart of the house, the statement “The house has a handle.” would beinaccurate as it relates to the teapot. As described in greater detailherein, diamond entailment patterns and ladder entailment patterns areimplemented in various embodiments through the implementation of “is a”and “has a” relationships between nodes in a universal cognitive graphto accurately maintain transitivity when modeling knowledge elements.

In these embodiments, as shown in FIGS. 12a through 12d , the creationof an “entity” node 1202 in a universal cognitive graph results in thecreation of a corresponding “attribute” node 1204 and an associated “hasa” relationship between the two. Likewise, the creation of the “whole”node 1206 results in the creation of a corresponding “attribute of awhole” node 1208. The “whole” node 1206 and the “attribute of a whole”node 1208 are respectively defined by the “entity” node 1202 and the“attribute” node 1204, as described in greater detail herein, throughcorresponding “is a” relationships. These “is a” relationships, incombination with the respective “has a” relationships between the“entity” node 1202, the “attribute” node 1204, the “whole” node 1206 andthe “attribute of a whole” node 1208 result in the formation of therungs and rails of a ladder entailment pattern, likewise described ingreater detail herein.

Likewise, the creation of a “set” node 1214 results in the creation ofan “attribute of set” node 1216 and a corresponding “has a” relationshipbetween the two. The “set 1204 and the “attribute of set node 1216 arerespectively defined by the “whole” node 1206 and the “attribute” node1204 through corresponding “is a” relationships. In turn, the creationof a “range (OR'ed order set)” node 1244 results in the creation of an“attribute of range” node 1226 and a corresponding “has a” relationship.Likewise, the “range” node 1244 and the “attribute of range” node 1226are respectively defined by the “set” node 1214 and the “attribute ofset” node 1216 by corresponding “is a” relationships.

These “is a” relationships, in combination with the respective “has a”relationships between the “set” node 1214, the “attribute of a set” node1216, the “range” node 1244, and the “attribute of range” node 1226likewise result in the formation of the rungs and rails of anotherladder entailment pattern. As a result, a numeric range of ‘0’ to ‘5’can be represented in the universal cognitive graph by the “0 to 5” node12415, and the measurement range of ‘8’ to ‘10’ feet can be representedby the “8 to 10 feet” node 1248, both of which are respectively definedby the “range” node 1244. Likewise, a numeric attribute of ‘0’ to ‘5’can be represented by the “attribute of 0 to 5” node 1250, which islikewise defined by the “attribute of range” node 1226.

Likewise, the creation of a “dimension” node 1252 results in thecreation of an “attribute of dimension” node 1262 and a corresponding“has a” relationship between the two. In turn, the “dimension” node 1252and the “attribute of dimension” node 1262 are respectively defined bythe “set” node 1214 and the “attribute of set” node 1216. As likewiseshown in FIGS. 12a through 12d , the creation of these corresponding“has a” and “is a” relationships respectively form rung and railextensions to the ladder entailment pattern previously formed by the“set” node 1214, the “attribute of a set” node 1216, the “range” node1244 and the “attribute of range” node 1226.

The creation of a “space” node 1258 likewise results in the creation ofa “attribute of space” node 1260 and a corresponding “has a”relationship between the two. The “space” node 1258 and the “attributeof space” node 1260 are respectively defined by the “dimension” node1252 and the “attribute of dimension” node 1262 through “is a”relationships. These “is a” relationships, in combination with therespective “has a” relationships between the “dimension” node 1252, the“attribute of dimension” node 1262, the “space” node 1258 and the“attribute of space” node 1260 result in the formation of the rungs andrails of yet another ladder entailment pattern.

Likewise, the creation of a “time” node 1254 results in the creation ofa “attribute of time” node 1250 and a corresponding “has a” relationshipbetween the two. The “time” node 1254 and the “attribute of time” node1260 are likewise respectively defined by the “dimension” node 1252 andthe “attribute of dimension” node 1262 through “is a” relationships.These “is a” relationships, in combination with the respective “has a”relationships between the “dimension” node 1252, the “attribute ofdimension” node 1262, the “space” node 1258 and the “attribute of space”node 1260 likewise result in the formation of the rungs and rails ofstill yet another ladder entailment pattern.

A part of time is likewise represented by the “part of time” node 1264,which is defined by the “attribute of time” node 1256 and the “part ofdimension” node 1264, both of which are defined by the “attribute ofdimension” node 1262. In turn, the “attribute of dimension” node 1262 isdefined by the “attribute of set” node 1216. As shown in FIGS. 12athrough 12d , the respective “is a” inheritance relationships betweenthe “part of time” node 1264, the “attribute of time” node 1256, the“part of dimension” node 1264, and the “attribute of dimension” node1262 result in the formation of a diamond entailment pattern, describedin greater detail herein.

Likewise, the “part of dimension” node 1264 is defined by the “part of aset” node 1218, which in turn is defined by the “attribute of set” node1216. The “part of a set” node 1218 is likewise defined by the “part”node 1210, which in turn is defined by “attribute of a whole” node 1208.By extension, the “part of not-a-set” node 1212 is likewise defined bythe “part” node 1212. The concept of duration is likewise represented bythe “duration” node 1280, which is defined by the “part of time” node1264 and the “subset” node 1232, which in turn is defined by both the“set” node 1214 and the “part of a set” node 1218. The “duration” node1280 is further defined by the “point in time” node 1278, which in turnis defined by the “part of time” node 1264 and the “point” node 1276.

In turn, the “point” node 1276 is defined by the “member” node 1220,which is defined by both the “part of a set” node 1218 and the “value”node 1222. Likewise, a subset of a range is represented by the “subsetof range” node 1230, which is defined by both the “subset” node 1232 andthe “part of a range” node 1228. A number is likewise represented by the“number” node 1224, which is defined by the “value” node, which in turnis defined by the “part of range” node 1228. Likewise, an upper limitand a lower limit are respectively represented by the “upper limit” node1240 and the “lower limit” node 1242, both of which are defined by the“limit” node 1228, which in turn is defined by the “value” node 1222.

A range, such as the numeric range of ‘0’ to ‘5’ is likewise representedby the “member of 0 to 5” node 1236, which is defined by the “member ofrange” node 1224, which in turn is represented by both the “member” node1220 and the “value” node 1222. Likewise, a particular area isrepresented by the “area” node 1270, which is defined by the “subset”node 1232 and the “location” node 12723, which in turn is defined by the“part of space” node 1268. The “area” node 1270 is further defined bythe “part of space” node 1268, which in turn is defined by the “part ofdimension” node 1254 and the “attribute of space” node 1260.

Accordingly, skilled practitioners of the art will recognize that theforegoing embodiments allow a reference to a particular point in time,such as “yesterday at 10 pm,” to be modeled as knowledge elements in theuniversal cognitive graph by the creation of corresponding node, such asthe “yesterday at 10 pm” node 1282. In various embodiments, the creationof the “yesterday at 10 pm” node 1282 results in the correspondingcreation of “is a” relationships to the “duration” node 1280 and the “apoint in time” node 1278. Likewise, a reference to a particularlocation, such as “right here,” can be modeled as knowledge elements ina universal cognitive graph through the creation of a correspondingnode, such as the “right here” node 1274. In certain embodiments, thecreation of the “right here” node 1274 results in the correspondingcreation of “is a” relationships to the “location” node 1272 and the“area” node 1270. In these embodiments, these “is a” relationships allowthe implementation of various entailment approaches described in greaterdetail herein, which in turn allow location, time and scale to beaccurately and precisely modeled as knowledge elements in a universalcognitive graph. Those of skill in the art will likewise recognize thatmany such embodiments are possible and the foregoing is not intended tolimit the spirit, scope or intent of the invention.

FIGS. 13a and 13b are a simplified graphical representation of verbsmodeled as knowledge elements in a universal cognitive graph implementedin accordance with an embodiment of the invention. Skilled practitionersof the art will be aware that certain semantic and cognitive linguistictheories postulate that verbs are the central power in a sentence or amental representation of an event, and as such, assign semantic roles tothe structures proximate to them. For example, any word filling thesubject position in the verb “run,” for example, plays a role of“agent”, rather than something like “time” or “experiencer.” Moreparticularly, no conceptualization or reference frame of running can bemade without an entity, such as a person, animal, or apparatus, playingthat part.

Those of skill in the art will likewise be aware that certain of thesetheories have yet to be applied to the complex problems posed by machinelearning and practical Natural Language Processing (NLP) applications.In part, this lack of utilization may be due to the realization that theextensive annotation commonly associated with these theories is unableto stand alone in a usable fashion. Likewise, these theories aretypically missing a backdrop of interconnectedness that allowsrelationships to be made and traversed in stored data. In variousembodiments, this lack of interconnectedness is addressed by combiningconceptual frames and theta roles with type theory.

As used herein, a conceptual frame broadly refers to a fundamentalrepresentation of knowledge in human cognition. In certain embodiments,a conceptual frame may include attribute-value sets, structuralinvariants, constraints, or some combination thereof. In variousembodiments, a conceptual frame may represent attributes, values,structural invariants, and constraints within the frame itself. Incertain of these embodiments, a conceptual frame may be builtrecursively. As likewise used herein, a theta role broadly refers to aformalized device for representing syntactic argument structure (i.e.,the number and type of noun phrases) required syntactically by aparticular verb. As such, theta roles are a syntactic notion of thenumber, type and placement of obligatory arguments.

Type theory, as used herein, broadly refers to a class of formalsystems, some of which can serve as alternatives to set theory as afoundation for mathematics. In type theory, every “term” has a “type”and operations are restricted to terms of a certain type. As an example,although “flee” and “run” are closely related, they have differentattributes. According to type theory, these attributes makes them uniqueconcepts. To continue the example, the theta roles that each verbassigns are a subset of their attributes, which means that the agent offleeing is a unique concept from the agent of running, though the sameperson or animal may play one or both parts.

Skilled practitioners of the art will recognize that since verbs are anopen class, there are an unlimited number of theta roles under thesepresuppositions. Each of the roles are related to the others, as theagent of flee could be said to inherit from the agent of run. Thisallows for the mapping of the structural elements around a verb (e.g., asubject, an object, a prepositional phrase complement, etc.) to betransformed into words higher up an inheritance chain implemented in auniversal cognitive graph. For example, the subject of “flee” is alsothe subject of “run” if the sentence is converted. As another example,the sentence, “You're running from something.” can likewise besyntactically and semantically converted into “You're fleeingsomething.”

As yet another example, if the concept of “sold goods” is related toboth the object of buying and the object of selling, then any time oneframe is invoked, so is the other. As yet still another example, in thesentence “I have a pie.” the role of the verb “have” might be “owner”and “attribute.” In this example, the attribute is a possession, but inanother example, the attribute might be a quality, such as “The lady hasgreat beauty.”

Likewise, the verb “be” is closely related to “have.” It also has anowner and an attribute. Furthermore, all attributes are both “had,” suchas in the noun sense, “I have beauty,” and “been,” such as in theadjective sense, “I am beautiful.” Moreover, any time any attribute isreferenced, such as “He paints so slowly!” the sentence may be rephrasedin terms of that verb, such as “His painting is so slow.” and “Hispainting has such slowness.” because all attributes are roles of “be.”In various embodiments, the combination of frame theory, or role theory,with set types permits the collection of multiple phrasings to be tiedto a single meaning. As a result, when a query is received by aCognitive Inference and Learning System (CILS), answers can be collectedin various embodiments across any possible phrasing for the concept.

For example, as shown in FIGS. 13a and 13b , the creation of an “entity”node 1302 in a universal cognitive graph results in the creation of acorresponding “attribute” node 1310 and an associated “has a”relationship between the two. As a result, a person named “Sue” can berepresented by the creation of a “Sue” node 1344, which is defined bythe “person” node 1306. Likewise, the “person” node is defined by the“animal” node 1304, which in turn is defined by the “entity” node 1302.The “attribute” node 1310 likewise defines the “attribute of verb” node1312, which in turn defines the “attribute of action” node 1316, whichlikewise defines the “attribute of movement” node 1320.

As likewise shown in FIGS. 13a and 13b , an action, such as “walk” isrepresented by the creation of a “walk” node 1326, which results in thecreation of a corresponding “attribute of walk” node 1328 and a “has a”relationship between the two. The “walk” node 1326 and the “attribute ofwalk” node 1328 are respectively defined by the “action” node 1308 andthe “attribute of movement” node 1320, as described in greater detailherein, through corresponding “is a” relationships. Likewise, the“semantic role” node 1314, the “role of action” node 1318, the “role ofmove” node 1322 and the “role of walk” node 1330 are respectivelydefined by the “attribute of verb” node 1312, the “attribute of action”node 1316, the “attribute of movement” node 1320 and the “attribute ofwalk” node 1328. The “role of walk” node 1330 is likewise defined by the“role of move” node 1322, which in turn is defined by the “role ofaction” node 1318, which is likewise defined by the “semantic role” node1314.

Likewise, the “agent of walk” node 1334 is defined by the “role of walk”node 1330 and the “agent of move” node 1324, which in turn is defined bythe “role of move” node 1322. The “destination of walk” node 1322 islikewise defined by the “role of walk” node 1330 and the “destination”node 1350, which in turn is defined by “role of move” node 1322.Likewise, the “destination” node 1350 is defined by the “location” node1348, which in turn is defined by the “entity” node 1302.

Accordingly, the statement “Sue walked to a store” can be represented bythe creation of a “Sue walked to a store” node 1336, which results inthe creation of the corresponding “attribute of Sue walked to a store”node 1338 and an associated “has a” relationship between the two. Asshown in FIG. 13b , the “Sue walked to a store” node 1336 and the“attribute of Sue walked to a store” node 1338 are respectively definedby the “walk” node 1326 and the “attribute of walk” node 1328 throughcorresponding “is a” relationships. These “is a” relationships, incombination with the respective “has a” relationships between the “walk”node 1326, the “attribute of walk” node 1328, the “Sue walked to astore” node 1336, and the “attribute of Sue walked to a store” node 1338result in the formation of the rungs and rails of a ladder entailmentpattern, described in greater detail herein.

Likewise, the “roles of Sue walked to a store” node 1340 is defined bythe “attribute of Sue walked to a store” node 1338 and the “role ofwalk” node 1330. In turn, the “destination of Sue walked to a store”node 1352 is defined by the “roles of Sue walked to a store” node 1340and the “destination of walk” node 1332. Likewise, the particular storethat Sue walked to can be represented by “this particular store” node1354, which is defined by both the “location” node 1348 and the“destination of Sue walked to a store” node 1352. The “Sue” node 1344 islikewise defined by “agent of Sue walked to a store” node 1346 m whichin turn is defined by the “agent of walk” node 1334.

Accordingly, skilled practitioners of the art will recognize that theforegoing embodiments allow the verb “walked” in the sentence, “Suewalked to a store” to be accurately modeled as knowledge elements in auniversal cognitive graph. Furthermore, the modeling of the verb“walked” results in an accurate portrayal of Sue's destination, which is“a store.” Those of skill in the art will likewise recognize that manysuch embodiments and examples are possible and the foregoing is notintended to limit the spirit, scope or intent of the invention.

FIGS. 14a and 14b are a simplified graphical representation of themodeling of negation in a universal cognitive graph implemented inaccordance with an embodiment of the invention. In various embodiments,a “not is a” relationship, which is a variant of an “is a” relationship,is implemented in a universal cognitive graph to accurately modelnegation. Skilled practitioners of the art will be aware of thechallenges negation poses in Natural Language Processing (NLP). One suchchallenge is that negation may be either phrasal (e.g., “I am not in thebuilding.”) or definitive (e.g., “I am absent from the building.”).Since either syntactic structure may indicate negation of the concept ofthe concept of “presence,” there is a need to accurately model both in auniversal cognitive graph.

Another challenge is the word “not” is typically omitted as a “stop”word in traditional search techniques as it appears too frequently.Furthermore, even when the word “not” is included, it is generally onthe basis of single-word searches. As a result, the concepts it modifiesare typically not taken into account. For example, performing a typicalInternet search for “cities not in Texas” returns any web page thatreferences cities in Texas and contains the word “not,” whichessentially provides search results that are the opposite of the user'sintent. Yet another challenge is that negation reverses entailment. Forexample, while the phrase “dogs eat bacon” entails “dogs eat meat,” thenegated form “dogs do not eat bacon” does not entail “dogs do not eatmeat.” Accordingly, such examples of reversed entailment need to beaccurately modeled in the universal cognitive graph such that that dataretrieval processes can traverse a known pattern of relationships.

In various embodiments negation may be modeled in a universal cognitivegraph by having a node encompass a negated concept, or alternatively, asa negated relationship between positive nodes. Those of skill in the artwill recognize the latter approach provides certain advantages, bothphilosophical and practical. For example, there is no practical need forthe human mind to store a concept for “not a dog.” More particularly,when the phrase is mentally conceptualized, all entities that are notdogs, an infinite class, are not brought to mind.

Alternatively, the negation of the positive class of “dog” is sufficientto understand the concept. Furthermore, the only defining feature of thenegative class of “not a dog” would be something that wasn't a dog. As aresult, the internal processes implemented for searching for childrennodes of a “not a dog” node would constantly be checking whether aconcept were a dog or not to determine whether it should haveinheritance into the node. In contrast, representing negation as a “notis a” relationship or edge in a universal cognitive graph results in theelimination of a host of nodes that would rarely be used. Furthermore,the structure of natural languages would be more closely followed.

In this embodiment, a person is represented by the “person” node 1406,which has a corresponding “has a” relationship with the “attribute ofperson” node 1408. Likewise, the “person” node 1406 is defined by the“animal” node” 1402, which has a corresponding “has a” relationship withthe “attribute of animal” node 1404, which in turn is used to define the“attribute of person” node 1408. These corresponding “has a” and “is a”relationships between the “person” node 1406, the “attribute of person”node 1408, the “animal” node” 1402, and the “attribute of animal” node1404 respectively form the rungs and rails of a ladder entailmentpattern, described in greater detail herein.

As shown in FIGS. 14a and 14b , a patient is represented by the“patient” node 1414, which has a corresponding “has a” relationship withthe “attribute of patient” node 1416. The “patient” node 1414 and the“attribute of patient” node 1416 are respectively defined by the“person” node 1406 and the “attribute of person” node 1408 through “isa” relationships. These corresponding “has a” and “is a” relationshipsrespectively form rung and rail extensions to the ladder entailmentpattern previously formed by the “animal” node” 1402, “attribute ofanimal” node 1404, the “person” node 1406, and the “attribute of person”node 1408. Likewise, a homeless person is represented by the “homelessperson” node 1418, which has a corresponding “has a” relationship withthe “attribute of homeless person” node 1420. The “homeless person” node1418 is defined by both the “person” node 1406 ad the “attribute ofperson” node 1408.

As likewise shown in FIGS. 14a and 14b , the concept of a homelesspatient is represented by the creation of the “homeless patient” node1428, which results in the creation of the “attribute of homelesspatient” node 1426 and a corresponding “has a” relationship between thetwo. Likewise, the “homeless patient” node 1428 is defined by the“homeless person” node 1418, the “patient” node 1414, and the “attributeof patient” node 1416 through “is a” relationships. The “attribute ofhomeless patient” node 1426 is likewise defined by the “attribute ofhomeless person” node 1420 through a corresponding “is a relationship.”As described in greater detail herein, these corresponding “has a” and“is a” relationships between the “homeless person” node 1418, the“attribute of homeless person” node 1420, the “homeless patient” node1428, and the “attribute of homeless patient” node 1426 form a ladderentailment pattern.

Likewise, the concept of a patient with a home is represented by the“patient with home” node 1430, which has a corresponding “has a”relationship with the “attribute of patient with home” node 1432. The“patient with home” node 1430 and the “attribute of patient with home”node 1432 are respectively defined by the “patient” node 1414 and the“attribute of patient” node 1416 through corresponding “is a”relationships. As shown in FIG. 14a , the “patient with home” node 1430is likewise defined by the “person with home” node 1410 with an “is a”relationship. As described in greater detail herein, these corresponding“has a” and “is a” relationships between the “patient” node 1414, the“attribute of patient” node 1416, the “patient with home” node 1430, andthe “attribute of patient with home” node 1432 form a ladder entailmentpattern.

The concept of an address associated with an animal can likewise berepresented by the “address of animal” node 1438, which is defined byboth the “address” node 1440 and the “attribute of animal” node 1404through corresponding “is a” relationships. In turn, the “address” node1438 is used to define the “address of person” node 1440, which islikewise used to define the “home address” node 1444, which has acorresponding “has a” relationship with the “attribute of home address”node 1446. In turn, the “home address” node 1444 is used to define the“patient's home address” node 1450, which is likewise defined by the“attribute of patient with home” node 1432. Likewise, the “home ofpatient” node 1434 has a corresponding “has a” relationship with the“attribute of patient's home address node” 1452, which is likewise usedto define the “patient's home zip code” node 1454. The “patient's homezip code” node 1454 is further defined by the “home zip code” node 1448,which in turn is defined by the “attribute of home address” node 1446.

As likewise shown in FIGS. 14a and 14b , the “person with home” node1410 has a corresponding “has a” relationship with the “attribute ofperson with home” node 1428, which is defined by the “attribute ofperson” node 1408. The “attribute of person with home” node 1428 islikewise used to define the “home” node 1430, which has a corresponding“has a” relationship with the “attribute of home” node 1432, which islikewise used to define the “home address” node 1444. Likewise, the“home of patient” node 1434 has a corresponding “has a” relationshipwith the “attribute of patient's home” node 1436, which is both definedby the “attribute of home” node 1432, and used to define the “patient'shome address” node 1450.

The “home of patient node” 1434 is likewise defined by the “attribute ofpatient with home” node” 1432 and the “home node” 1430. As described ingreater detail herein, the various “has a” and “is a” relationshipsbetween the “home node” 1430, the “attribute of patient” node 1416, the“home of patient node” 1434, and the “attribute of patient's home” node1436 form a ladder entailment pattern. From the foregoing, skilledpractitioners of the art will recognize that further defining the “home”node 1430 with a “not is a” relationship with the “attribute of homelessperson” node 1420 simply and accurately implements the use of negationto support the concept that a homeless patient does not have a home.Those of skill in the art will likewise recognize that many suchembodiments are possible and the foregoing is not intended to limit thespirit, scope or intent of the invention.

FIGS. 15a through 15e are a simplified graphical representation of acorpus of text modeled as knowledge elements in a universal cognitivegraph implemented in accordance with an embodiment of the invention torepresent an associated natural language concept. In variousembodiments, text data is conceptualized through the performance ofvarious Natural Language Processing (NLP) parsing, instance mapping, andresolution processes described in greater detail herein. In theseembodiments, the performance of these processes results in thegeneration of various knowledge elements, which are then stored as nodesin a universal cognitive graph. The performance of these processeslikewise results in the generation of graph edges representing various“is a” and “has a” relationships, which support inheritance andattribution properties, likewise described in greater detail herein. Asa result, text received by a Cognitive Inference and Learning System(CILS), whether a statement or a query, is accurately and preciselymodeled within the universal cognitive graph.

In various embodiments, a natural language concept is defined in auniversal cognitive graph by describing the necessary and sufficientconditions for membership or inheritance of that type. For example:

-   -   a victim is any entity that is harmed    -   boating is traveling on a boat

Each of these definitions defines a parent and a restriction on thatparent. In this example, the word “victim” is any entity filling theexperiencer role of the verb “harm.” In another example, the word“victim” is filling the experiencer role of the verb “harm:”

“The chemical harmed the wallpaper.”

-   -   ->“The wallpaper is a victim of the chemical you used.”

As yet another example:

“The four of them traveled to Hawaii on a cruise ship.”

-   -   ->“The four of them boated to Hawaii.”

In this example, the phrase “to boat” modifies its parent, “travel,”such that whenever the mode of “travel” is a “boat,” the sentence may berephrased with the verb “boat.”

In various embodiments, deductions are made by examining the necessaryand sufficient criteria for inheritance of the type. If the criteria aremet by a particular concept, a new “is a” relationship is created. Forexample:

“Maggie worked serving food to rich clients at a country club.”

In this example, an NLP parser implemented in various embodiments wouldidentify that “Maggie” refers to a woman. As a result, a new noderepresenting that instance of “Maggie” would be created duringconceptualization and an “is a” relationship would be created between itand a node representing the class “woman,” which is defined throughinheritance from a node representing the class “human.” To continue theexample, a subclass of the class “human” might be “employee.” In variousembodiments, a conceptualizer is implemented, which would identify theconcept of “worked serving food,” and search for nodes that use thatstatement, or any part thereof, as part of a definition. To furthercontinue the example, “waitress,” a type of employee, might be definedas an agent of the concept of “worked serving food.” Accordingly, sincethe “Maggie” node meets the necessary and sufficient criteria for a“waitress” node, an “is a” relationship can be extrapolated between themand created.

In various embodiments, any instance or class can fold downward on anentailment tree implemented in a universal cognitive graph. Furthermore,every type can be subtyped infinitely. For example, if a node inheritsfrom a type, and matches the criteria for a subtype, it folds downward,removing the “is a” relationship from the original class, and adding an“is a” relationship to the subtype. In these embodiments, the more thatis discovered about a concept, the further down an entailment tree theconcept can be classed, thereby increasing its specificity.

In certain embodiments, upward folding only occurs at storage time, notat the time a query is processed. As an example, the following statementwould be stored as an instance of “hasten:”

“Maggie hastened to serve the senator his lunch.”

-   -   “Did Maggie serve the senator quickly?”    -   ->yes

Since hasten is defined as to act quickly, the manner of the “hasten”frame element inherits from the manner of “act,” and adds the additionalattribute of “quick.” When the second sentence, “Did Maggie serve thesenator quickly?” is received by a CILS, it is stored as an instance of“serve,” which in turn is an instance of “act.” Downward folding alongthe definition of “hasten” leads the sentence to be stored as “DidMaggie hasten to serve the senator?” which in turn leads directly to thefirst sentence, which is stored as, “Maggie hastened to serve thesenator his lunch.”

In various embodiments, unstructured text data sourced into a CILS isfirst normalized and then mapped to the conceptual model of theuniversal cognitive graph. In these embodiments, the unstructured textdata is ingested into the CILS in one of the following ways:

-   -   base knowledge    -   unstructured text parsing    -   from a mapper for an existing sourcing agent    -   from a mapper for an existing enrichment agent    -   rule-based internal resolution

In various embodiments, enrichment processes, described in greaterdetail herein, are performed on a corpus of text with utilizablestructure received by a CILS. In these embodiments, these blocks of textwithin the corpus are stored in their native forms in a graph, firsttheir context, then the document they reside in, then the positionwithin the document, and finally the structural components of the textitself, such as headers, paragraphs, sentences, phrases, words,phonemes. Those of skill in the art will be aware that no readable textis ever truly unstructured, as comprehension requires at least some ofthese substructures to exist. As a result, various ingestion enginesimplemented in certain embodiments merely store these substructures in amachine-readable manner in the universal cognitive graph.

Once the existing architecture is captured, additional structure isextrapolated as a suggested parse, which is stored in the universalcognitive graph alongside the text itself. Skilled practitioners of theart will recognize that everything up to this point can be said to beinfallible, or true to its source, yet the parse is fallible, andintroduces the potential for error. Accordingly, the text iscomponentized and improved by machine learning approaches familiar tothose of skill in the art.

The goal of parsing operations performed in these embodiments is to mapthe text strings to a set of known words and phrase structures. Fromthere, each word or phrase is resolved or disambiguated. In particular,a disambiguated word or phrase points to only one concept or meaning. Ifthe parse or resolution is incorrect, then the resulting “understanding”(i.e., the proper placement in the universal cognitive graph) will alsobe incorrect, just as human communication has the potential formisinterpretation. The result of these parsing and resolution processesis conceptualization, which is as used herein, refers to the mapping andstorage of each structure, from context down to the phoneme level, toits own node in the universal cognitive graph, and by extension, linkedto the existing nodes with appropriate entailment and attributionrelationships.

In various embodiments, the CILS is implemented with an NLP engine thatis configured to select various NLP parsers, as needed, based upon theirrespective features and capabilities, to optimize performance andaccuracy of modeling knowledge elements in the universal cognitivegraph. In these embodiments, the CILS annotates the parsed text toidentify the parser used to parse the text in the universal cognitivegraph, including both the part of speech tags and the parse tree (e.g.,typed dependencies, trees, or other forms representing the parsestructure).

As an example, an NLP parser may receive the following query forprocessing:

“What is a good restaurant with a patio?”

As a result, each typed dependency in the parser may represent a phrase,resulting in the following output:

attr(is-2, what-1),

root(ROOT-0, is-2),

det(restaurant-5, a-3),

amod(restaurant-5, good-4),

nsubj(is-2, restaurant-5),

det(patio-8, a-7),

prep_with(restaurant-5, patio-8)

In this example, the set of typed dependencies, including det, nsubj,cop, and root, needs to be accurately modeled in the ontology of theuniversal cognitive graph. Against this typed-dependency backdrop, thetextual data is then sent to the parser, tagged with parts of speech andtyped dependencies, and exported into the graph according to its tags.Accordingly, the phase is pre-computed, and then saved in the universalcognitive graph before a related query or piece of textual data isprocessed.

Once text is ingested and assimilated into the universal cognitivegraph, resolution processes are performed in various embodiments to mapthe concept to individual nodes. In these embodiments, the resolutionprocesses may involve traversal from text, through representation, toconcept. In certain embodiments, the text stage is bypassed during theresolution process. In various embodiments, the resolution processes maybe ongoing as the CILS may resolve incompletely or incorrectly and haveto go back and revisit the original text when prompted.

Skilled practitioners of the art will be aware that there are a varietyof techniques that when combined, can generate increasingly usefulresolutions. As an example, a first stage of resolution can be testedindependently from the parse and assimilation stages by iteration overeach word, or token, in the case of multiwords and morphemes, in thesentence, document, or context, to find the set of nodes which have thetext string as their name, while omitting stop words. As an example, theword “park,” would likely yield two nodes: “park,” where one relaxes,and “park,” which is related to storing a vehicle.

In certain embodiments, a simple score can be affixed to each sense byfinding the degree of separation between any given sense of a word, andall the senses of each other word in the context. Likewise, the scoringand ranking algorithms implemented within these embodiments may bemanipulated to achieve optimum results, and machine learning approachesfamiliar to those of skill in the art may be used long-term. Those ofskill in the art will likewise recognize that it is advantageous tostore the decision tree should there be a need to backtrack at any pointand re-resolve to correct an error. For example, if a user communicatessomething to the effect of “That isn't what I meant.” In theseembodiments, these approaches may be used at any stage as it relates toan ontology implemented in the universal graph, with the understandingthat as the ontology broadens, the scoring will become more accurate.

In various embodiments, each word received and processed by a CILS hasboth syntactic and semantic restrictions upon its distribution inspeech. For example, the preposition “in,” when resolving to a sensemeaning “duration,” requires the complemented noun phrase following ithave the concept of a length of time or duration. For example, thephrase “two hours” in “She accomplished the task in two hours.” In theseembodiments, affixes, word type, position, case, and other markers mayeither restrict an ambiguity set or ultimately resolve a word to itsconcept. Skilled practitioners of the art will recognize that this stageof processing will require an accurate parse to represent each word'sparticular distribution.

Referring now to FIGS. 15a through 15e , a phrase is represented by the“phrase” node 1501, which is used to define the “dependent” node 1504,the “main phrase” node” 1503, the “root” node 1502, and the “nounphrase” node 1504 through “is a” relationships, as described in greaterdetail herein. The “dependent” node 1504 is likewise used to define the“auxiliary” node 1505, which in turn is used to define both the “passiveauxiliary” node 1506 and the “copula” node 1507. Likewise, the“dependent” node 1504 is used to define the “argument” node 1508, whichin turn is used to define the “referent” node 1509, the “agent” node1510, and the “subject” node 1511, which in turn is used to define the“clausal subject” node 1512, and the “nominal subject” node 1514.

In turn, the “clausal subject” node 1512 and the “nominal subject” node1514 are respectively used to define the “passive clausal subject” node1513 and the “passive nominal subject” node 1515. Likewise, the“argument” node 1508 is used to define the “coordination” node 1516, the“expletive” node 1517, the “parataxis” node 1518, the “punctuation” node1519, the “conjunct” node 1520, and the “semantic dependent” node 1521,which in turn is used to define the “controlling subject” node 1522.

The “argument” node 1508 is likewise used to define the “modifier” node1523, which in turn is used to define the “adverbial clause modifier”node 1524, the “marker” node 1525, the “phrasal verb particle” node 1526and the “possessive modifier(s)” node 1527. Likewise, the “modifier”node 1523 is used to define the “possession modifier(s)” node 1528, the“preconjunct” node 1529, the “element of compound number” node 1530, the‘quantifier modifier” node 1531, and the “non-compound modifier” node1532. The “modifier” node 1523 is likewise used to define the “numericmodifier” node 1533, the “relative clause modifier” node 1634, and the“noun phrase adverbial modifier” node 1535, which in turn is used todefine the “temporal modifier” node 1536.

Likewise, the “modifier” node 1523 is used to modify the “appositional”node 1537 and the “adverbial modifier” node 1538, which in turn is usedto define the “negation modifier” node 1539. The “modifier” node 1523 islikewise used to define the “predeterminer” node 1541, the “multi-wordexpression modifier” node 1542, the “reduce, non-finite verbal modifier”node 1543, and the “propositional modifier” node 1540, which has acorresponding “has a” relationship with the “object of proposition” node1551. Likewise, the “modifier” node 1523 is used to define the“determiner” node 1558 and the “adjectival modifier” 1559.

As shown in FIGS. 15a through 15e , the “argument” node 1508 is likewiseused to define the “complement” node 1544, which in turn is used todefine the “clausal complement with internal subject” node 1547, and the“adjective complement” node 1546. Likewise, the “argument” node 1508 isused to define the “attributive (between what and be)” node 1545, the“clausal complement with external subject” node 1553, and the “object”node 1548. In turn, the “object” node 1548 is used to define the “directobject” node 1549, the “indirect object” node 1550, and the “object ofpreposition” node 1551, which is likewise used to define the “(objectof) prep_with” node 1552.

The “noun phrase” node 1554 is likewise used to define the “noun phrasecomplemented by preposition phrase” node 1555, which in turn is used todefine the “noun phrase complemented by ‘WITH’ preposition phrase” node1556, which has a corresponding “has a” relationship with the “phraseheaded by ‘WITH’ modifier” node 1557. In turn, the “phrase headed by‘WITH’ modifier” node 1557 is defined by the “propositional modifier”node 1540, and likewise has a corresponding “has a” relationship withthe “(object of) prep_with” node 1552.

From the foregoing, those of skill in the art will recognize that aphrase, such as “What is a good restaurant with a patio?” can berepresented by the creation of a corresponding node in the universalcognitive graph, such as the “What is a good restaurant with a patio?”node 1560, which in turn is defined by the “main phrase” node 1503. Inthis embodiment, NLP parsing processes, described in greater detailherein, are performed to generate parsed phrase segments from the phrase“What is a good restaurant with a patio?” The resulting phrase segmentsare then represented by creating corresponding nodes within theuniversal cognitive graph shown in FIGS. 15a through 15e . For example,the parsed phrase segments “What,” “is,” “a,” “good,” and “restaurant,”are respectively represented by the “phrase segment ‘1’ ‘what’” node1551, the “phrase segment ‘2’ ‘is’” node 1562, the “phrase segment ‘3’‘a’” node 1563, the “phrase segment ‘4’ ‘good’” node 1564, and the“phrase segment ‘5’ ‘restaurant’” node 1565. Likewise, the “with,” “a,”and “patio” parsed phrase segments are respectively represented by the“phrase segment ‘6’ ‘with” node 1566, the “phrase segment ‘7’ ‘a’” node1567, and the “phrase segment ‘8’ ‘patio’” node 1567.

As likewise shown in FIGS. 15a through 15e , the various relationshipsbetween these nodes are represented by “is a” and “has a” relationships,as described in greater detail herein. For example, the “What is a goodrestaurant with a patio?” node 1560 has a corresponding “has a”relationship with the “phrase segment ‘2’ ‘is’” node 1562, which isdefined by the “root” node 1502. The “phrase segment ‘2’ ‘is’” node 1562likewise has corresponding “has a” relationships with the “phrasesegment ‘5’ ‘restaurant’” node 1565 and the “phrase segment ‘1’ ‘what’”node 1551, which is defined by the “attribute (between what and be)”node 1545. The “phrase segment ‘5’ ‘restaurant’” node 1565 is likewisedefined by the “noun phrase” node 1554, the “noun phrase complemented by‘WITH’ preposition phrase” node 1556 and the “nominal subject” node1514. Likewise, the “phrase segment ‘5’ ‘restaurant’” node 1565 hascorresponding “has a” relationships with the “phrase segment ‘3’ ‘a’”node 1563, the “phrase segment ‘4’ ‘good’” node 1564.

In turn, the “phrase segment ‘3’ ‘a’” node 1563 is defined by the“determiner” node 1558, and the “phrase segment ‘4’ ‘good’” node 1564 isdefined by the “adjectival modifier” node 1559. The “phrase segment ‘6’‘with” node 1566 is likewise defined by the “phrase headed by ‘WITH’modifier” node 1557, and has a corresponding “has a” relationship withthe “phrase segment ‘8’ ‘patio’” node 1567. In turn, the “phrase segment‘8’ ‘patio’” node 1567 is likewise defined by the “(object of)prep_with” node 1552, and has a corresponding “has a” relationship withthe “phrase segment ‘7’ ‘a’” node 1567, which in turn is defined by the“determiner” node 1558.

In various embodiments, parsed phrase segments resulting from NLPparsing operations can be resolved to accurately represent a naturallanguage concept associated with a phrase received by a CILS forprocessing. In these embodiments, predetermined natural language conceptrelationships between individual parsed phrase segments andcorresponding natural language concepts are generated. As an example, anoun phrase that is complemented by a “with” preposition phrase, like“tree with leaves,” is a representation of the concept “thing whichhas.” The object of that prepositional phrase, “tree with leaves,” is arepresentation of the natural language concept “thing which is had.”Accordingly, the phrase segment “restaurant with patio” can be mapped to“restaurant that has a patio,” and “patio” can be mapped to “the patiothat is had by the restaurant.”

In certain embodiments, the distinction between natural languagerepresentation and natural language concept spaces effectivelymodularizes syntax and semantics without actually separating them.Syntax is described by the nodes in the natural language representationof knowledge elements in the universal cognitive graph, while semanticsis described by a natural language concept space. In variousembodiments, the natural language concept space is implemented within auniversal cognitive graph. In these embodiments, any number of languagesor formats of representation, such as photographs, documents, Frenchlexical units, and so forth, will point to the same natural languageconcepts regardless of their source.

In this embodiment, a natural language concept 1590 for the phrase “Whatis a good restaurant with a patio?” can be resolved by first creating a“concept” node 1570. In turn, the “concept” node 1570 is used to definethe “owner (possess)” node 1571, the “have (possess)” node 1573, the“attribute of possession” node 1575, the “patio” node 1577, and the“restaurant” node 1578. The “have (possess) node 1573 likewise hascorresponding “has a” relationships with the “owner (possess)” node 1571and the “attribute of possession” node 1575, and is likewise used todefine the “good restaurant has patio” node 1574. Likewise, the “goodrestaurant has patio” node 1574 has corresponding “has a” relationshipswith the “good restaurant with patio” node 1572, which is defined by the“owner (possess) node 1571, and “patio of good restaurant” node 1776,which defined by both the “attribute of possession” node 1575 and the“patio” node 1574. The “good restaurant with patio” node 1580 islikewise defined by the “good restaurant with patio” node 1572 and the“restaurant” node 1578.

As shown in FIGS. 15a through 15e , the “owner (possess)” node 1571 isresolved by establishing a predetermined natural language conceptrelationship with the “noun phrase complemented by ‘WITH’ prepositionphrase” node 1556. The “attribute of possession” concept node 1575 islikewise resolved by establishing a predetermined natural languageconcept relationship with the “(object of) prep_with” node 1552. Theresolution of the “good restaurant with patio” node 1572 is likewiseensured by establishing a predetermined natural language conceptrelationship with the “phrase segment ‘5’ ‘restaurant’” node 1565.Likewise, the resolution of the “good restaurant has patio” node 1574,and the “patio of good restaurant” node 1576 are ensured by respectivelyestablishing predetermined natural language concept relationships withthe “phrase segment ‘6’ ‘with’” node 1566, and the “phrase segment ‘8’‘patio’” node 1567. Skilled practitioners of the art will recognize thatmany such embodiments are possible and the foregoing is not intended tolimit the spirit, scope or intent of the invention.

FIG. 16 is a simplified block diagram of a plurality of cognitiveplatforms implemented in accordance with an embodiment of the inventionwithin a hybrid cloud infrastructure. In this embodiment, the hybridcloud infrastructure 1640 includes a cognitive cloud management 342component, a hosted 1604 cognitive cloud environment, and a private 1606network environment. As shown in FIG. 16, the hosted 1604 cognitivecloud environment includes a hosted 1610 cognitive platform, such as thecognitive platform 310 shown in FIGS. 3, 4 a, and 4 b. In variousembodiments, the hosted 1604 cognitive cloud environment may alsoinclude a hosted 1617 universal knowledge repository and one or morerepositories of curated public data 1614 and licensed data 1616.Likewise, the hosted 1610 cognitive platform may also include a hosted1612 analytics infrastructure, such as the cloud analyticsinfrastructure 344 shown in FIGS. 3 and 4 c.

As likewise shown in FIG. 16, the private 1606 network environmentincludes a private 1620 cognitive platform, such as the cognitiveplatform 310 shown in FIGS. 3, 4 a, and 4 b. In various embodiments, theprivate 1606 network cognitive cloud environment may also include aprivate 1628 universal knowledge repository and one or more repositoriesof application data 1624 and private data 1626. Likewise, the private1620 cognitive platform may also include a private 1622 analyticsinfrastructure, such as the cloud analytics infrastructure 344 shown inFIGS. 3 and 4 c. In certain embodiments, the private 1606 networkenvironment may have one or more private 1636 cognitive applicationsimplemented to interact with the private 1620 cognitive platform.

As used herein, a universal knowledge repository broadly refers to acollection of knowledge elements that can be used in various embodimentsto generate one or more cognitive insights described in greater detailherein. In various embodiments, these knowledge elements may includefacts (e.g., milk is a dairy product), information (e.g., an answer to aquestion), descriptions (e.g., the color of an automobile), skills(e.g., the ability to install plumbing fixtures), and other classes ofknowledge familiar to those of skill in the art. In these embodiments,the knowledge elements may be explicit or implicit. As an example, thefact that water freezes at zero degrees centigrade would be an explicitknowledge element, while the fact that an automobile mechanic knows howto repair an automobile would be an implicit knowledge element.

In certain embodiments, the knowledge elements within a universalknowledge repository may also include statements, assertions, beliefs,perceptions, preferences, sentiments, attitudes or opinions associatedwith a person or a group. As an example, user ‘A’ may prefer the pizzaserved by a first restaurant, while user ‘B’ may prefer the pizza servedby a second restaurant. Furthermore, both user ‘A’ and ‘B’ are firmly ofthe opinion that the first and second restaurants respectively serve thevery best pizza available. In this example, the respective preferencesand opinions of users ‘A’ and ‘B’ regarding the first and secondrestaurant may be included in the universal knowledge repository 880 asthey are not contradictory. Instead, they are simply knowledge elementsrespectively associated with the two users and can be used in variousembodiments for the generation of various cognitive insights, asdescribed in greater detail herein.

In various embodiments, individual knowledge elements respectivelyassociated with the hosted 1617 and private 1628 universal knowledgerepositories may be distributed. In one embodiment, the distributedknowledge elements may be stored in a plurality of data stores familiarto skilled practitioners of the art. In this embodiment, the distributedknowledge elements may be logically unified for various implementationsof the hosted 1617 and private 1628 universal knowledge repositories. Incertain embodiments, the hosted 1617 and private 1628 universalknowledge repositories may be respectively implemented in the form of ahosted or private universal cognitive graph. In these embodiments, nodeswithin the hosted or private universal graph contain one or moreknowledge elements.

In various embodiments, a secure tunnel 1630, such as a virtual privatenetwork (VPN) tunnel, is implemented to allow the hosted 1610 cognitiveplatform and the private 1620 cognitive platform to communicate with oneanother. In these various embodiments, the ability to communicate withone another allows the hosted 1610 and private 1620 cognitive platformsto work collaboratively when generating cognitive insights described ingreater detail herein. In various embodiments, the hosted 1610 cognitiveplatform accesses knowledge elements stored in the hosted 1617 universalknowledge repository and data stored in the repositories of curatedpublic data 1614 and licensed data 1616 to generate various cognitiveinsights. In certain embodiments, the resulting cognitive insights arethen provided to the private 1620 cognitive platform, which in turnprovides them to the one or more private cognitive applications 1636.

In various embodiments, the private 1620 cognitive platform accessesknowledge elements stored in the private 1628 universal knowledgerepository and data stored in the repositories of application data 1624and private data 1626 to generate various cognitive insights. In turn,the resulting cognitive insights are then provided to the one or moreprivate cognitive applications 1636. In certain embodiments, the private1620 cognitive platform accesses knowledge elements stored in the hosted1617 and private 1628 universal knowledge repositories and data storedin the repositories of curated public data 1614, licensed data 1616,application data 1624 and private data 1626 to generate variouscognitive insights. In these embodiments, the resulting cognitiveinsights are in turn provided to the one or more private cognitiveapplications 1636.

In various embodiments, the secure tunnel 1630 is implemented for thehosted 1610 cognitive platform to provide 1632 predetermined data andknowledge elements to the private 1620 cognitive platform. In oneembodiment, the provision 1632 of predetermined knowledge elementsallows the hosted 1617 universal knowledge repository to be replicatedas the private 1628 universal knowledge repository. In anotherembodiment, the provision 1632 of predetermined knowledge elementsallows the hosted 1617 universal knowledge repository to provide updates1634 to the private 1628 universal knowledge repository. In certainembodiments, the updates 1634 to the private 1628 universal knowledgerepository do not overwrite other data. Instead, the updates 1634 aresimply added to the private 1628 universal knowledge repository.

In one embodiment, knowledge elements that are added to the private 1628universal knowledge repository are not provided to the hosted 1617universal knowledge repository. As an example, an airline may not wishto share private information related to its customer's flights, theprice paid for tickets, their awards program status, and so forth. Inanother embodiment, predetermined knowledge elements that are added tothe private 1628 universal knowledge repository may be provided to thehosted 1617 universal knowledge repository. As an example, the operatorof the private 1620 cognitive platform may decide to licensepredetermined knowledge elements stored in the private 1628 universalknowledge repository to the operator of the hosted 1610 cognitiveplatform. To continue the example, certain knowledge elements stored inthe private 1628 universal knowledge repository may be anonymized priorto being provided for inclusion in the hosted 1617 universal knowledgerepository. In one embodiment, only private knowledge elements arestored in the private 1628 universal knowledge repository. In thisembodiment, the private 1620 cognitive platform may use knowledgeelements stored in both the hosted 1617 and private 1628 universalknowledge repositories to generate cognitive insights. Skilledpractitioners of the art will recognize that many such embodiments arepossible and the foregoing is not intended to limit the spirit, scope orintent of the invention.

FIGS. 17a and 17b are a simplified process flow diagram showing the useof a universal knowledge repository by a Cognitive Inference andLearning System (CILS) implemented in accordance with an embodiment ofthe invention to generate composite cognitive insights. As used herein,a composite cognitive insight broadly refers to a set of cognitiveinsights generated as a result of orchestrating a set of independentcognitive agents, referred to herein as insight agents. In variousembodiments, the insight agents use a cognitive graph, such as anapplication cognitive graph 1782, as their data source to respectivelygenerate individual cognitive insights. As used herein, an applicationcognitive graph 1782 broadly refers to a cognitive graph that isassociated with a cognitive application 304. In certain embodiments,different cognitive applications 304 may interact with differentapplication cognitive graphs 1782 to generate individual cognitiveinsights for a user. In various embodiments, the resulting individualcognitive insights are then composed to generate a set of compositecognitive insights, which in turn is provided to a user in the form of acognitive insight summary 1748.

In various embodiments, the orchestration of the selected insight agentsis performed by the cognitive insight/learning engine 330 shown in FIGS.3 and 4 a. In certain embodiments, a subset of insight agents isselected to provide composite cognitive insights to satisfy a graphquery 1744, a contextual situation, or some combination thereof. Forexample, it may be determined, as likewise described in greater detailherein, that a particular subset of insight agents may be suited toprovide a composite cognitive insight related to a particular user of aparticular device, at a particular location, at a particular time, for aparticular purpose.

In certain embodiments, the insight agents are selected fororchestration as a result of receiving direct or indirect input data1742 from a user. In various embodiments, the direct user input may be anatural language inquiry. In certain embodiments, the indirect userinput data 1742 may include the location of a user's device or thepurpose for which it is being used. As an example, the GeographicalPositioning System (GPS) coordinates of the location of a user's mobiledevice may be received as indirect user input data 1742. As anotherexample, a user may be using the integrated camera of their mobiledevice to take a photograph of a location, such as a restaurant, or anitem, such as a food product. In certain embodiments, the direct orindirect user input data 1742 may include personal information that canbe used to identify the user. Skilled practitioners of the art willrecognize that many such embodiments are possible and the foregoing isnot intended to limit the spirit, scope or intent of the invention.

In various embodiments, composite cognitive insight generation andfeedback operations may be performed in various phases. In thisembodiment, these phases include a data lifecycle 1736 phase, a learning1738 phase, and an application/insight composition 1740 phase. In thedata lifecycle 1736 phase, a predetermined instantiation of a cognitiveplatform 1710 sources social data 1712, public data 1714, licensed data1716, and proprietary data 1718 from various sources as described ingreater detail herein. In various embodiments, an example of a cognitiveplatform 1710 instantiation is the cognitive platform 310 shown in FIGS.3, 4 a, and 4 b. In this embodiment, the instantiation of a cognitiveplatform 1710 includes a source 1706 component, a process 1708component, a deliver 1730 component, a cleanse 1720 component, an enrich1722 component, a filter/transform 1724 component, and a repair/reject1726 component. Likewise, as shown in FIG. 17b , the process 1708component includes a repository of models 1728, described in greaterdetail herein.

In various embodiments, the process 1708 component is implemented toperform various composite insight generation and other processingoperations described in greater detail herein. In these embodiments, theprocess 1708 component is implemented to interact with the source 1706component, which in turn is implemented to perform various data sourcingoperations described in greater detail herein. In various embodiments,the sourcing operations are performed by one or more sourcing agents, aslikewise described in greater detail herein. The resulting sourced datais then provided to the process 1708 component. In turn, the process1708 component is implemented to interact with the cleanse 1720component, which is implemented to perform various data cleansingoperations familiar to those of skill in the art. As an example, thecleanse 1720 component may perform data normalization or pruningoperations, likewise known to skilled practitioners of the art. Incertain embodiments, the cleanse 1720 component may be implemented tointeract with the repair/reject 1726 component, which in turn isimplemented to perform various data repair or data rejection operationsknown to those of skill in the art.

Once data cleansing, repair and rejection operations are completed, theprocess 1708 component is implemented to interact with the enrich 1722component, which is implemented in various embodiments to performvarious data enrichment operations described in greater detail herein.Once data enrichment operations have been completed, the process 1708component is likewise implemented to interact with the filter/transform1724 component, which in turn is implemented to perform data filteringand transformation operations described in greater detail herein.

In various embodiments, the process 1708 component is implemented togenerate various models, described in greater detail herein, which arestored in the repository of models 1728. The process 1708 component islikewise implemented in various embodiments to use the sourced data togenerate one or more cognitive graphs, such as an application cognitivegraph 1782, as likewise described in greater detail herein. In variousembodiments, the process 1708 component is implemented to gain anunderstanding of the data sourced from the sources of social data 1712,public data 1714, device data 1716, and proprietary data 1718, whichassist in the automated generation of the application cognitive graph1782.

The process 1708 component is likewise implemented in variousembodiments to perform bridging 1746 operations, described in greaterdetail herein, to access the application cognitive graph 1782. Incertain embodiments, the bridging 1746 operations are performed bybridging agents, likewise described in greater detail herein. In variousembodiments, the application cognitive graph 1782 is accessed by theprocess 1708 component during the learn 1736 phase of the compositecognitive insight generation operations.

In various embodiments, a cognitive application 304 is implemented toreceive input data associated with an individual user or a group ofusers. In these embodiments, the input data 1742 may be direct, such asa user query or mouse click, or indirect, such as the current time orGeographical Positioning System (GPS) data received from a mobile deviceassociated with a user. In various embodiments, the indirect input data1742 may include contextual data, described in greater detail herein.Once it is received, the input data 1742 is then submitted by thecognitive application 304 to a graph query engine 326 during theapplication/insight composition 1740 phase.

The submitted 1742 input data is then processed by the graph queryengine 326 to generate a graph query 1744, as described in greaterdetail herein. The resulting graph query 1744 is then used to query theapplication cognitive graph 1782, which results in the generation of oneor more composite cognitive insights, likewise described in greaterdetail herein. In certain embodiments, the graph query 1744 usesknowledge elements stored in the universal knowledge repository 1780when querying the application cognitive graph 1782 to generate the oneor more composite cognitive insights.

In various embodiments, the graph query 1744 results in the selection ofa cognitive persona from a repository of cognitive personas ‘1’ through‘n’ 1772 according to a set of contextual information associated with auser. As used herein, a cognitive persona broadly refers to an archetypeuser model that represents a common set of attributes associated with ahypothesized group of users. In various embodiments, the common set ofattributes may be described through the use of demographic, geographic,psychographic, behavioristic, and other information. As an example, thedemographic information may include age brackets (e.g., 25 to 34 yearsold), gender, marital status (e.g., single, married, divorced, etc.),family size, income brackets, occupational classifications, educationalachievement, and so forth. Likewise, the geographic information mayinclude the cognitive persona's typical living and working locations(e.g., rural, semi-rural, suburban, urban, etc.) as well ascharacteristics associated with individual locations (e.g., parochial,cosmopolitan, population density, etc.).

The psychographic information may likewise include information relatedto social class (e.g., upper, middle, lower, etc.), lifestyle (e.g.,active, healthy, sedentary, reclusive, etc.), interests (e.g., music,art, sports, etc.), and activities (e.g., hobbies, travel, going tomovies or the theatre, etc.). Other psychographic information may berelated to opinions, attitudes (e.g., conservative, liberal, etc.),preferences, motivations (e.g., living sustainably, exploring newlocations, etc.), and personality characteristics (e.g., extroverted,introverted, etc.) Likewise, the behavioristic information may includeinformation related to knowledge and attitude towards variousmanufacturers or organizations and the products or services they mayprovide.

In various embodiments, one or more cognitive personas may be associatedwith a user. In certain embodiments, a cognitive persona is selected andthen used by a CILS to generate one or more composite cognitive insightsas described in greater detail herein. In these embodiments, thecomposite cognitive insights that are generated for a user as a resultof using a first cognitive persona may be different than the compositecognitive insights that are generated as a result of using a secondcognitive persona.

In various embodiments, provision of the composite cognitive insightsresults in the CILS receiving feedback 1762 data from various individualusers and other sources, such as cognitive application 304. In oneembodiment, the feedback 1762 data is used to revise or modify thecognitive persona. In another embodiment, the feedback 1762 data is usedto create a new cognitive persona. In yet another embodiment, thefeedback 1762 data is used to create one or more associated cognitivepersonas, which inherit a common set of attributes from a sourcecognitive persona. In one embodiment, the feedback 1762 data is used tocreate a new cognitive persona that combines attributes from two or moresource cognitive personas. In another embodiment, the feedback 1762 datais used to create a cognitive profile, described in greater detailherein, based upon the cognitive persona. Those of skill in the art willrealize that many such embodiments are possible and the foregoing is notintended to limit the spirit, scope or intent of the invention.

In certain embodiments, the universal knowledge repository 1780 includesthe repository of personas ‘1’ through ‘n’ 1772. In various embodiments,individual nodes within cognitive personas stored in the repository ofpersonas ‘1’ through ‘n’ 1772 are linked 1754 to corresponding nodes inthe universal knowledge repository 1780. In certain embodiments, nodeswithin the universal knowledge repository 1780 are likewise linked 1754to nodes within the cognitive application graph 1782.

As used herein, contextual information broadly refers to informationassociated with a location, a point in time, a user role, an activity, acircumstance, an interest, a desire, a perception, an objective, or acombination thereof. In certain embodiments, the contextual informationis likewise used in combination with the selected cognitive persona togenerate one or more composite cognitive insights for a user. In variousembodiments, the composite cognitive insights that are generated for auser as a result of using a first set of contextual information may bedifferent than the composite cognitive insights that are generated as aresult of using a second set of contextual information.

As an example, a user may have two associated cognitive personas,“purchasing agent” and “retail shopper,” which are respectively selectedaccording to two sets of contextual information. In this example, the“purchasing agent” cognitive persona may be selected according to afirst set of contextual information associated with the user performingbusiness purchasing activities in their office during business hours,with the objective of finding the best price for a particular commercialinventory item. Conversely, the “retail shopper” cognitive persona maybe selected according to a second set of contextual informationassociated with the user performing cognitive personal shoppingactivities in their home over a weekend, with the objective of finding adecorative item that most closely matches their current furnishings.Those of skill in the art will realize that the composite cognitiveinsights generated as a result of combining the first cognitive personawith the first set of contextual information will likely be differentthan the composite cognitive insights generated as a result of combiningthe second cognitive persona with the second set of contextualinformation.

In various embodiments, the graph query 1744 results in the selection ofa cognitive profile from a repository of cognitive profiles ‘1’ through‘n’ 1774 according to identification information associated with a user.As used herein, a cognitive profile refers to an instance of a cognitivepersona that references personal data associated with a user. In variousembodiments, the personal data may include the user's name, address,Social Security Number (SSN), age, gender, marital status, occupation,employer, income, education, skills, knowledge, interests, preferences,likes and dislikes, goals and plans, and so forth. In certainembodiments, the personal data may include data associated with theuser's interaction with a CILS and related composite cognitive insightsthat are generated and provided to the user.

In various embodiments, the personal data may be distributed. In certainof these embodiments, subsets of the distributed personal data may belogically aggregated to generate one or more cognitive profiles, each ofwhich is associated with the user. In various embodiments, the user'sinteraction with a CILS may be provided to the CILS as feedback 1762data. Skilled practitioners of the art will recognize that many suchembodiments are possible and the foregoing is not intended to limit thespirit, scope or intent of the invention.

In various embodiments, a cognitive persona or cognitive profile isdefined by a first set of nodes in a weighted cognitive graph. In theseembodiments, the cognitive persona or cognitive profile is furtherdefined by a set of attributes that are respectively associated with aset of corresponding nodes in the weighted cognitive graph. In variousembodiments, an attribute weight is used to represent a relevance valuebetween two attributes. For example, a higher numeric value (e.g.,‘5.0’) associated with an attribute weight may indicate a higher degreeof relevance between two attributes, while a lower numeric value (e.g.,‘0.5’) may indicate a lower degree of relevance.

In various embodiments, the numeric value associated with attributeweights may change as a result of the performance of composite cognitiveinsight and feedback 1762 operations described in greater detail herein.In one embodiment, the changed numeric values associated with theattribute weights may be used to modify an existing cognitive persona orcognitive profile. In another embodiment, the changed numeric valuesassociated with the attribute weights may be used to generate a newcognitive persona or cognitive profile. In these embodiments, acognitive profile is selected and then used by a CILS to generate one ormore composite cognitive insights for the user as described in greaterdetail herein. In certain of these embodiments, the results of the oneor more cognitive learning operations may likewise provide a basis foradaptive changes to the CILS, and by extension, the composite cognitiveinsights it generates.

In various embodiments, provision of the composite cognitive insightsresults in the CILS receiving feedback 1762 information related to anindividual user. In one embodiment, the feedback 1762 information isused to revise or modify a cognitive persona. In another embodiment, thefeedback 1762 information is used to revise or modify the cognitiveprofile associated with a user. In yet another embodiment, the feedback1762 information is used to create a new cognitive profile, which inturn is stored in the repository of cognitive profiles ‘1’ through ‘n’1774. In still yet another embodiment, the feedback 1762 information isused to create one or more associated cognitive profiles, which inherita common set of attributes from a source cognitive profile. In anotherembodiment, the feedback 1762 information is used to create a newcognitive profile that combines attributes from two or more sourcecognitive profiles. In various embodiments, these persona and profilemanagement operations 1776 are performed through interactions betweenthe cognitive application 304, the repository of cognitive personas ‘1’through ‘n’ 1772, the repository of cognitive profiles ‘1’ through ‘n’1774, the universal knowledge repository 1780, or some combinationthereof. Those of skill in the art will realize that many suchembodiments are possible and the foregoing is not intended to limit thespirit, scope or intent of the invention.

In various embodiments, a cognitive profile associated with a user maybe either static or dynamic. As used herein, a static cognitive profilerefers to a cognitive profile that contains identification informationassociated with a user that changes on an infrequent basis. As anexample, a user's name, Social Security Number (SSN), or passport numbermay not change, although their age, address or employer may change overtime. To continue the example, the user may likewise have a variety offinancial account identifiers and various travel awards programidentifiers which change infrequently.

As likewise used herein, a dynamic cognitive profile refers to acognitive profile that contains information associated with a user thatchanges on a dynamic basis. For example, a user's interests andactivities may evolve over time, which may be evidenced by associatedinteractions with the CILS. In various embodiments, these interactionsresult in the provision of associated composite cognitive insights tothe user. In these embodiments, the user's interactions with the CILS,and the resulting composite cognitive insights that are generated, areused to update the dynamic cognitive profile on an ongoing basis toprovide an up-to-date representation of the user in the context of thecognitive profile used to generate the composite cognitive insights.

In various embodiments, a cognitive profile, whether static or dynamic,is selected from the repository of cognitive profiles ‘1’ through ‘n’1774 according to a set of contextual information associated with auser. In certain embodiments, the contextual information is likewiseused in combination with the selected cognitive profile to generate oneor more composite cognitive insights for the user. In one embodiment,the composite cognitive insights that are generated as a result of usinga first set of contextual information in combination with the selectedcognitive profile may be different than the composite cognitive insightsthat are generated as a result of using a second set of contextualinformation with the same cognitive profile. In various embodiments, oneor more cognitive profiles may be associated with a user. In certainembodiments, the composite cognitive insights that are generated for auser as a result of using a set of contextual information with a firstcognitive profile may be different than the composite cognitive insightsthat are generated as a result of using the same set of contextualinformation with a second cognitive profile.

As an example, a user may have two associated cognitive profiles,“runner” and “foodie,” which are respectively selected according to twosets of contextual information. In this example, the “runner” cognitiveprofile may be selected according to a first set of contextualinformation associated with the user being out of town on businesstravel and wanting to find a convenient place to run close to where theyare staying. To continue this example, two composite cognitive insightsmay be generated and provided to the user in the form of a cognitiveinsight summary 1248. The first may be suggesting a running trail theuser has used before and liked, but needs directions to find again. Thesecond may be suggesting a new running trail that is equally convenient,but wasn't available the last time the user was in town.

Conversely, the “foodie” cognitive profile may be selected according toa second set of contextual information associated with the user being athome and expressing an interest in trying either a new restaurant or aninnovative cuisine. To further continue this example, the user's“foodie” cognitive profile may be processed by the CILS to determinewhich restaurants and cuisines the user has tried in the last eighteenmonths. As a result, two composite cognitive insights may be generatedand provided to the user in the form of a cognitive insight summary1748. The first may be a suggestion for a new restaurant that is servinga cuisine the user has enjoyed in the past. The second may be asuggestion for a restaurant familiar to the user that is promoting aseasonal menu featuring Asian fusion dishes, which the user has nottried before. Those of skill in the art will realize that the compositecognitive insights generated as a result of combining the firstcognitive profile with the first set of contextual information willlikely be different than the composite cognitive insights generated as aresult of combining the second cognitive profile with the second set ofcontextual information.

In various embodiments, a user's cognitive profile, whether static ordynamic, may reference data that is proprietary to the user, anorganization, or a combination thereof. As used herein, proprietary databroadly refers to data that is owned, controlled, or a combinationthereof, by an individual user or an organization, which is deemedimportant enough that it gives competitive advantage to that individualor organization. In certain embodiments, the organization may be agovernmental, non-profit, academic or social entity, a manufacturer, awholesaler, a retailer, a service provider, an operator of a cognitiveinference and learning system (CILS), and others.

In various embodiments, an organization may or may not grant a user theright to obtain a copy of certain proprietary information referenced bytheir cognitive profile. In certain embodiments, a first organizationmay or may not grant a user the right to obtain a copy of certainproprietary information referenced by their cognitive profile andprovide it to a second organization. As an example, the user may not begranted the right to provide travel detail information (e.g., traveldates and destinations, etc.) associated with an awards program providedby a first travel services provider (e.g., an airline, a hotel chain, acruise ship line, etc.) to a second travel services provider. In variousembodiments, the user may or may not grant a first organization theright to provide a copy of certain proprietary information referenced bytheir cognitive profile to a second organization. Those of skill in theart will recognize that many such embodiments are possible and theforegoing is not intended to limit the spirit, scope or intent of theinvention.

In various embodiments, a set of contextually-related interactionsbetween a cognitive application 304 and the application cognitive graph1782 are represented as a corresponding set of nodes in a cognitivesession graph, which is then stored in a repository of cognitive sessiongraphs ‘1’ through ‘n’ 1752. As used herein, a cognitive session graphbroadly refers to a cognitive graph whose nodes are associated with acognitive session. As likewise used herein, a cognitive session broadlyrefers to a user, group of users, theme, topic, issue, question, intent,goal, objective, task, assignment, process, situation, requirement,condition, responsibility, location, period of time, or any combinationthereof.

As an example, the application cognitive graph 1782 may be unaware of aparticular user's preferences, which are likely stored in acorresponding user profile. To further the example, a user may typicallychoose a particular brand or manufacturer when shopping for a given typeof product, such as cookware. A record of each query regarding thatbrand of cookware, or its selection, is iteratively stored in acognitive session graph that is associated with the user and stored in arepository of cognitive session graphs ‘1’ through ‘n’ 1752. As aresult, the preference of that brand of cookware is ranked higher, andis presented in response to contextually-related queries, even when thepreferred brand of cookware is not explicitly referenced by the user. Tocontinue the example, the user may make a number of queries over aperiod of days or weeks, yet the queries are all associated with thesame cognitive session graph that is associated with the user and storedin a repository of cognitive session graphs ‘1’ through ‘n’ 1752,regardless of when each query is made.

As another example, a user may submit a query to a cognitive application304 during business hours to find an upscale restaurant located closetheir place of business. As a result, a first cognitive session graphstored in a repository of cognitive session graphs ‘1’ through ‘n’ 1752is associated with the user's query, which results in the provision ofcomposite cognitive insights related to restaurants suitable forbusiness meetings. To continue the example, the same user queries thesame cognitive application 304 during the weekend to locate a casualrestaurant located close to their home. As a result, a second cognitivesession graph stored in a repository of cognitive session graphs ‘1’through ‘n’ 1752 is associated with the user's query, which results inthe provision of composite cognitive insights related to restaurantssuitable for family meals. In these examples, the first and secondcognitive session graphs are both associated with the same user, but fortwo different purposes, which results in the provision of two differentsets of composite cognitive insights.

As yet another example, a group of customer support representatives istasked with resolving technical issues customers may have with aproduct. In this example, the product and the group of customer supportrepresentatives are collectively associated with a cognitive sessiongraph stored in a repository of cognitive session graphs ‘1’ through ‘n’1752. To continue the example, individual customer supportrepresentatives may submit queries related to the product to a cognitiveapplication 304, such as a knowledge base application. In response, acognitive session graph stored in a repository of cognitive sessiongraphs ‘1’ through ‘n’ 1752 is used, along with the universal knowledgerepository 1780 and application cognitive graph 1782, to generateindividual or composite cognitive insights to resolve a technical issuefor a customer. In this example, the cognitive application 304 may bequeried by the individual customer support representatives at differenttimes during some time interval, yet the same cognitive session graphstored in a repository of cognitive session graphs ‘1’ through ‘n’ 1752is used to generate composite cognitive insights related to the product.

In various embodiments, each cognitive session graph associated with auser and stored in a repository of cognitive session graphs ‘1’ through‘n’ 1752 includes one or more direct or indirect user queriesrepresented as nodes, and the time at which they were asked, which arein turn linked 1754 to nodes that appear in the application cognitivegraph 1782. In certain embodiments, each individual cognitive sessiongraph that is associated with the user and stored in a repository ofcognitive session graphs ‘1’ through ‘n’ 1752 introduces edges that arenot already present in the application cognitive graph 1782. Morespecifically, each of the cognitive session graphs that is associatedwith the user and stored in a repository of cognitive session graphs ‘1’through ‘n’ 1752 establishes various relationships that the applicationcognitive graph 1782 does not already have.

In various embodiments, individual cognitive profiles in the repositoryof cognitive profiles ‘1’ through ‘n’ 1774 are respectively stored ascognitive session graphs in the repository of cognitive session graphs1752. In these embodiments, nodes within each of the individualcognitive profiles are linked 1754 to nodes within correspondingcognitive session graphs stored in the repository of cognitive sessiongraphs ‘1’ through ‘n’ 1754. In certain embodiments, individual nodeswithin each of the cognitive profiles are likewise linked 1754 tocorresponding nodes within various cognitive personas stored in therepository of cognitive personas ‘1’ through ‘n’ 1772.

In various embodiments, individual graph queries 1744 associated with asession graph stored in a repository of cognitive session graphs ‘1’through ‘n’ 1752 are likewise provided to insight agents to performvarious kinds of analyses. In certain embodiments, each insight agentperforms a different kind of analysis. In various embodiments, differentinsight agents may perform the same, or similar, analyses. In certainembodiments, different agents performing the same or similar analysesmay be competing between themselves.

For example, a user may be a realtor that has a young, uppermiddle-class, urban-oriented clientele that typically enjoys eating attrendy restaurants that are in walking distance of where they live. As aresult, the realtor may be interested in knowing about new or popularrestaurants that are in walking distance of their property listings thathave a young, middle-class clientele. In this example, the user'squeries may result the assignment of insight agents to perform analysisof various social media interactions to identify such restaurants thathave received favorable reviews. To continue the example, the resultingcomposite insights may be provided as a ranked list of candidaterestaurants that may be suitable venues for the realtor to meet hisclients.

In various embodiments, the process 1708 component is implemented toprovide these composite cognitive insights to the deliver 1730component, which in turn is implemented to deliver the compositecognitive insights in the form of a cognitive insight summary 1748 tothe cognitive application 304. In these embodiments, the cognitiveplatform 1710 is implemented to interact with an insight front-end 1756component, which provides a composite insight and feedback interfacewith the cognitive application 304. In certain embodiments, the insightfront-end 1756 component includes an insight Application ProgramInterface (API) 1758 and a feedback API 1760, described in greaterdetail herein. In these embodiments, the insight API 1758 is implementedto convey the cognitive insight summary 1748 to the cognitiveapplication 304. Likewise, the feedback API 1760 is used to conveyassociated direct or indirect user feedback 1762 to the cognitiveplatform 1710. In certain embodiments, the feedback API 1760 providesthe direct or indirect user feedback 1762 to the repository of models1728 described in greater detail herein.

To continue the preceding example, the user may have received a list ofcandidate restaurants that may be suitable venues for meeting hisclients. However, one of his clients has a pet that they like to takewith them wherever they go. As a result, the user provides feedback 1762that he is looking for a restaurant that is pet-friendly. The providedfeedback 1762 is in turn provided to the insight agents to identifycandidate restaurants that are also pet-friendly. In this example, thefeedback 1762 is stored in the appropriate cognitive session graph 1752associated with the user and their original query.

In various embodiments, the composite insights provided in eachcognitive insight summary 1748 to the cognitive application 304, andcorresponding feedback 1762 received from a user in return, is providedto an associated cognitive session graph 1752 in the form of one or moreinsight streams 1764. In these and other embodiments, the insightstreams 1764 may contain information related to the user of thecognitive application 304, the time and date of the provided compositecognitive insights and related feedback 1762, the location of the user,and the device used by the user.

As an example, a query related to upcoming activities that is receivedat 10:00 AM on a Saturday morning from a user's home may returncomposite cognitive insights related to entertainment performancesscheduled for the weekend. Conversely, the same query received at thesame time on a Monday morning from a user's office may return compositecognitive insights related to business functions scheduled during thework week. In various embodiments, the information contained in theinsight streams 1764 is used to rank the composite cognitive insightsprovided in the cognitive insight summary 1748. In certain embodiments,the composite cognitive insights are continually re-ranked as additionalinsight streams 1764 are received. Skilled practitioners of the art willrecognize that many such embodiments are possible and the foregoing isnot intended to limit the spirit, scope or intent of the invention.

Although the present invention has been described in detail, it shouldbe understood that various changes, substitutions and alterations can bemade hereto without departing from the spirit and scope of the inventionas defined by the appended claims.

What is claimed is:
 1. A computer-implementable method for ingestinginformation into a cognitive graph comprising: receiving data from adata source; determining whether the data comprises text; ingesting thedata, the ingesting comprising performing a natural language processingoperation on the data, the ingesting the data identifying a plurality ofknowledge elements based upon the natural language processing operation;and, storing at least some of the knowledge elements within thecognitive graph as a collection of knowledge elements, the storinguniversally representing knowledge obtained from the data.
 2. The methodof claim 1, wherein: the ingesting further comprises identifying theplurality of knowledge elements by performing a parsing operation on thetext, the parsing operation producing a plurality of parse trees.
 3. Themethod of claim 2, wherein: the processing further comprising performinga machine learning operation on the plurality of knowledge elements, themachine learning operation resolving which knowledge elements within theparse tree provide a best result representing a meaning of the text, themachine learning operation identifying knowledge elements of theplurality of parse trees representing ambiguous portions of the text. 4.The method of claim 3, wherein: the machine learning operation resolvesthe plurality of parse trees to a best tree representing aninterpretation of the ambiguous portions of the text.
 5. The method ofclaim 2, wherein: the processing further comprises performing aconceptualization operation on the plurality of knowledge elements, theconceptualization operation identifying a relationship of conceptsidentified from within the plurality of parse trees produced via theparsing operation and storing knowledge elements within the cognitivegraph in a configuration representing the relationship of concepts. 6.The method of claim 5, wherein: the conceptualization operationidentifies the relationships of the concepts using information storedwithin the cognitive graph.